Supplementary methods

Schematics for replacement of the unknown Sxl-GFP nuclear background with the isogenic w1118 background

Figure S1: Crossing scheme used to create a standard homozygous GFP-w line. Males from this line were crossed with females carrying a specific mitochondrial haplotype, to create experimental mitolines. These newly produced lines carried the mitochondrial haplotype of the female and were heterozygous for the Sxl-GFP construct. G1 = the first generation of the cross.

Figure S1: Crossing scheme used to create a standard homozygous GFP-w line. Males from this line were crossed with females carrying a specific mitochondrial haplotype, to create experimental mitolines. These newly produced lines carried the mitochondrial haplotype of the female and were heterozygous for the Sxl-GFP construct. G1 = the first generation of the cross.

Data analysis and supplementary results

Here we include all code used to run our analysis, our rationale behind the modelling approaches, and all remaining supplementary tables and figures.

Load packages, read in the data and create some helpful functions

EDIT THE PACKAGE LIST WHEN HAPPY WITH THE ANALYSIS

# load relevant packages

library(lme4) # for the lmer and glmer mixed model functions
library(lmerTest) # Used to get p-values for lmer models using simulation. It over-writes lmer() with a new version, which gives p-values
library(glmmTMB) # for zero-inflated or hurdle glms
library(MuMIn) # for model selection and averaging
library(tidyverse) # data re-shaping, ggplot, stringr and more
library(ggridges) # for joy plots
library(ggpubr) # for the ggarrange function
library(ggbeeswarm) # violin plots with data points
library(ggResidpanel) # for model assumption plots
library(kableExtra) # nice tables that can scroll
library(pander) # more nice tables
library(groupdata2) # for assigning rows in data-frames to groups

# Read in data frame and add Dyad_ID column

all_data <- read.csv("mtDNA_larval_competition_data.csv") %>% 
  arrange(Individual) %>%
  group(n = 2, method = "greedy") %>% rename(Dyad_ID = .groups)

# helper function for saving large model objects and naming the file object.rds

save_it <- function(object){
  saveRDS(get(object), file = paste(object, ".rds", sep = ""))}

Data preparation for all responses

# Clean the dataset up for analysis

# Select the columns we're interested in and rename them

fitness_data <- dplyr::select(all_data, Individual, Block, Strain,  Dyad_ID, Sex, Focal.haplotype, Social.haplotype, Mortality, Development.time..hrs., Wing.size..mm., Female.offspring, Male.offspring, Total.female.assay, Total.red.all.vials, Total.bw.all.vials, Proportion.red.all.vials) %>% 
  
rename(Block = Block, Survived = Mortality, Focal_haplotype = Focal.haplotype, Social_haplotype = Social.haplotype, Dev_time = Development.time..hrs., Wing_length = Wing.size..mm., Maternal_female_offspring = Female.offspring, Maternal_male_offspring = Male.offspring, Maternal_total_offspring = Total.female.assay, Paternal_focal_offspring = Total.red.all.vials, Paternal_bw_offspring = Total.bw.all.vials, Proportion_focal = Proportion.red.all.vials)

# Define new levels for mortality to make renaming possible 

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "NO")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "YES")

# Rename the mortality responses
# L means died as larva, P means died as pupae, N means did not die (i.e. eclosed as an adult)

fitness_data$Survived[fitness_data$Survived == 'L'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'P'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'N'] <- 'YES'

# Now that it makes sense change "YES" to 1 and "NO" to 0 so we can fit a binomial GLM.

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "1")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "0")

fitness_data$Survived[fitness_data$Survived == "YES"] <- 1
fitness_data$Survived[fitness_data$Survived == "NO"] <- 0

# Make the factor numeric 

fitness_data$Survived <- as.numeric(as.character(fitness_data$Survived))


# Create specific datasets for each fitness trait

# Remove all rows that contain an NA value in the survival column. The NAs mean things like the GFP sorting did not work, or the vial was never set up due to a shortage of larvae. They are not meaningful data, and we remove them here.

survival <- fitness_data %>% filter(!is.na(Survived)) 
  
# Remove all rows that contain an NA value in the development time column. This instances represent flies where we failed to measure development time. 

larval_development <- fitness_data %>% filter(!is.na(Dev_time)) 

# Remove all rows that contain an NA value in the wing length column. Wing length was not measured in Blocks 1 and 2.

body_size <- fitness_data %>% filter(!is.na(Wing_length)) 

# Remove all rows that contain an NA value in the female reproductive output column (e.g. all the males), and where females did not survive to adulthood (coded as producing 0 offspring). 

female_reproductive_output <- fitness_data %>% filter(!is.na(Maternal_total_offspring), Survived == 1)


# Male adult fitness

# First remove females from the dataset.

Male_fitness <- fitness_data %>% filter(!is.na(Paternal_focal_offspring)) 

# Create an offspring counted column so that the data is correctly formatted for a binomial success-failure model.

Male_fitness$Offspring_counted <- Male_fitness$Paternal_focal_offspring + Male_fitness$Paternal_bw_offspring

# Now lets remove vials where the female produced 0 offspring (this includes trials where the male died in development), as we cannot determine paternity from these vials. The tidy up the dataframe by removing unneccessary columns

Male_fitness <- Male_fitness %>% filter(!(Offspring_counted == 0)) %>% 
  select(-Maternal_female_offspring, -Maternal_male_offspring, -Maternal_total_offspring) %>% 
  rename(Focal_male_offspring = Paternal_focal_offspring, bw_male_offspring = Paternal_bw_offspring)

Modelling approach

We analysed the data using generalised linear mixed models in the lmer package for R.

Fixed effects

For the analysis of fitness traits expressed in both sexes (survival, development time and body size), we are interested in the effect of an individual’s focal mtDNA, the mtDNA of a social competitor and the effect of sex on fitness. To identify these potential effects each model contained the following fixed effects and the three-way interaction between these variables:

Focal haplotype: the mtDNA haplotype that an individual carries.

Social haplotype: the mtDNA haplotype carried by a social partner during larval development.

Sex: is the focal individual female or male? The social partner’s sex was always opposite to that of the focal individual.

Random effects

Duplicate strain: Each haplotype has been introgressed alongside the w1118 nuclear background in two independent duplicates, creating 10 total strains. Within each block we ran multiple replicates that were made up of flies from the first set of strains (i.e. Barcelona 1, Brownsville 1 etc.), while the other half used only flies from strains denoted ‘2’. This random effect accounts for any residual differences in their nuclear genome, epigenome, microbiome or vial environment that may have arisen between duplicates.

Block: accounts for differences in the response variable between experimental blocks (e.g. to variance in temperature or composition of the fly food). In our experiment a block contained multiple replicates and a replicate was made up of 25 different cells each housing a pair of larvae.

Dyad ID: accounts for differences in the quality of the larval environment between pairs of larvae. For example, the moisture content of the food varied between pipette tips, despite our best efforts to keep this variable constant.

Model evaluation

Each model was evaluated and ranked by AICc values using the dredge function, from the Mumin package. There was rarely a single model that was unequivocally the best fit to the data, so we conducted model averaging for the set of models where delta was < 6, as suggested by Symonds and Moussalli (2011). The present study is a planned experiment to measure the effect of mtDNA on fitness, so we derived model estimates from the conditional model averages.

Larval fitness measures

Egg to adult viability analysis


We fit a glm with binomial errors to model survival

The model:

Survival ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

# Fit the global model

survival_model <- lme4::glmer(Survived ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

Model evaluation

Table S1: Evaluation of the survivorship model. All possible models were evaluated from the global model that included a three-way interaction between Focal haplotype, Social haplotype and Sex, as well as the random factors duplicate Strain, Block and Dyad ID. As there was no clear top model, the final model was calculated via model averaging.

# Compare all possible combinations of models (from the global model)

if(file.exists("survival_dredge.rds")){ # If already done, just load the results
  survival_dredge <- readRDS("survival_dredge.rds")
} else {survival_dredge <- dredge(survival_model) # If not already done, run all the models and save the results
lapply(c("survival_dredge"), save_it)
}


survival_table <- subset(survival_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(survival_table)[names(survival_table) == "(Intercept)"] <- "Intercept"
names(survival_table)[names(survival_table) == "Focal_haplotype"] <- "Focal haplotype"
names(survival_table)[names(survival_table) == "Sex"] <- "Sex"
names(survival_table)[names(survival_table) == "Social_haplotype"] <- "Social haplotype"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(survival_table)[names(survival_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(survival_table)[names(survival_table) == "df"] <- "Degrees of freedom"
names(survival_table)[names(survival_table) == "logLik"] <- "Log likelihood"
names(survival_table)[names(survival_table) == "AICc"] <- "AICc"
names(survival_table)[names(survival_table) == "delta"] <- "Delta"
names(survival_table)[names(survival_table) == "weight"] <- "Weight"

pander(survival_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
1 -0.225 NA NA NA NA NA NA NA 4 -1304 2617 0 0.4529
3 -0.2715 NA + NA NA NA NA NA 5 -1304 2618 1.066 0.2658
2 -0.1177 + NA NA NA NA NA NA 8 -1302 2619 2.469 0.1318
4 -0.1639 + + NA NA NA NA NA 9 -1301 2621 3.557 0.0765
5 -0.2554 NA NA + NA NA NA NA 8 -1303 2622 5.416 0.0302

Model averaging

Conditional model coefficients, standard error and 95% confidence limits listed in Table 1 are shown for the survivorship to adulthood averaged model. Bold rows indicate significant effects.

# Model average

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict(), fitted() or eemeans()

top_survival_models <- get.models(survival_dredge, subset = delta < 6)

survival_avgm <- model.avg(top_survival_models)


# extract useful information

RVI_survival <- MuMIn::sw(survival_dredge)

# average the models with delta < 6

survival_CIs <- confint(model.avg(survival_dredge, subset = delta < 6)) %>% as.data.frame()

survival_estimate <- coefTable(model.avg(survival_dredge, subset = delta < 6)) %>% as.data.frame()

survival_model_avg <- data.frame(survival_estimate, survival_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(survival_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(survival_model_avg)[names(survival_model_avg) == "Estimate"] <- "Conditional average estimate"
names(survival_model_avg)[names(survival_model_avg) == "Std..Error"] <- "Standard Error"
names(survival_model_avg)[names(survival_model_avg) == "X2.5.."] <- "2.5% Interval"
names(survival_model_avg)[names(survival_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(survival_model_avg, split.cell = 40, split.table = Inf, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval
Intercept -0.219 0.309 -0.825 0.387
Sex: Male 0.093 0.095 -0.094 0.279
Focal haplotype: Brownsville -0.084 0.151 -0.38 0.213
Focal haplotype: Dahomey 0.08 0.151 -0.216 0.376
Focal haplotype: Israel -0.261 0.153 -0.561 0.039
Focal haplotype: Sweden -0.214 0.154 -0.515 0.088
Social haplotype: Brownsville -0.073 0.152 -0.371 0.224
Social haplotype: Dahomey 0.039 0.152 -0.258 0.336
Social haplotype: Israel 0.167 0.152 -0.13 0.464
Social haplotype: Sweden 0.021 0.153 -0.279 0.321
# The full average provides a parameter average across all models considered, including ones where the parameter coefficient is set to 0. The conditional average reports coefficents for only the models where the parameter is included.

Development time analysis


(
density_development_plot <- ggplot(larval_development)+
  stat_density_ridges(aes(x=Dev_time, y = NA, fill = Sex), alpha = 0.7, scale = 12, position = position_nudge(y = -0.5), show.legend = T) +
  geom_vline(xintercept = 238, linetype = 2) +
  geom_vline(xintercept = 262, linetype = 2) +
  geom_vline(xintercept = 286, linetype = 2) +
  xlab("Egg-to-adult development time (hours)") +
  ylab("Kernel density estimate") +
  theme_bw() +
  scale_fill_manual(values = c("F" = "#ca562c", "M" = "#008080"), labels = c("Female", "Male")) +
  scale_x_continuous(limits = c(220, 310), breaks = c(220, 230, 240, 250, 260, 270, 280, 290, 300, 310)) +
  scale_y_discrete(expand = c(.0,0.0))+
  theme(panel.spacing = unit(0.1, "lines"),
        text = element_text(size=16),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.x = element_text(hjust = 0.5, size = 14))
)

Figure S2: The distribution of egg-to-adult development time, split by sex. Dashed lines indicate when lights were turned each morning and highlight the relationship between light and eclosion.

The response variable has a trimodal distribution, that can be potentially explained by the lab’s artificial day-night cycle. When the lights turn on at 7am, eclosion is stimulated.

The model:

Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

# Fit the linear model

linear_dev_model <- lmer(Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

Lets have a look at model diagnostics

resid_panel(linear_dev_model)

The data is trimodal and the residuals vs fitted plot indicates that the mean and variance are weakly positively correlated. The Q-Q Plot shows that points fall off at the extremes, but generally conform to a linear pattern. Linear models are somewhat robust against slightly non-normal data so we proceed with the analysis.

Model evaluation

Table S2: Evaluation of the development time model. All possible models were evaluated from the global model that included a three-way interaction between Focal haplotype, Social haplotype and Sex as well as the random factors Strain and Block. As there was no clear top model, the final model was calculated via model averaging.

# Use dredge to compare all possible models derived from the global model

Dev_time_linear_dredge <- dredge(linear_dev_model)

development_table <- subset(Dev_time_linear_dredge, delta < 6, recalc.weights = FALSE)  %>% as.data.frame()

names(development_table)[names(development_table) == "(Intercept)"] <- "Intercept"
names(development_table)[names(development_table) == "Focal_haplotype"] <- "Focal haplotype"
names(development_table)[names(development_table) == "Social_haplotype"] <- "Social haplotype"
names(development_table)[names(development_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(development_table)[names(development_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(development_table)[names(development_table) == "df"] <- "Degrees of freedom"
names(development_table)[names(development_table) == "logLik"] <- "Log likelihood"
names(development_table)[names(development_table) == "AICc"] <- "AICc"
names(development_table)[names(development_table) == "delta"] <- "Delta"
names(development_table)[names(development_table) == "weight"] <- "Weight"

pander(development_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
3 260.5 NA + NA NA NA NA NA 6 -2998 6008 0 0.7286
1 261.6 NA NA NA NA NA NA NA 5 -3001 6012 3.858 0.1059
4 261.9 + + NA NA NA NA NA 10 -2996 6012 3.98 0.09962
7 260.1 NA + + NA NA NA NA 10 -2997 6014 5.995 0.03637

Model averaging

Conditional model coefficients, standard error and 95% confidence limits listed in Table 2 are shown for the egg-to-adult development time averaged model. Bold rows indicate significant effects.

# first get the RVI for each predictor

RVI_dev <- MuMIn::sw(Dev_time_linear_dredge)

# Model averaging

Dev_time_avg <- (model.avg(Dev_time_linear_dredge, subset = delta < 6))

Dev_CIs <- confint(model.avg(Dev_time_linear_dredge, subset = delta < 6)) %>% as.data.frame()

Dev_estimate <- coefTable(model.avg(Dev_time_linear_dredge, subset = delta < 6)) %>% as.data.frame()

Dev_model_avg <- data.frame(Dev_estimate, Dev_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Dev_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Dev_model_avg)[names(Dev_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Dev_model_avg)[names(Dev_model_avg) == "Std..Error"] <- "Standard Error"
names(Dev_model_avg)[names(Dev_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Dev_model_avg)[names(Dev_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(Dev_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval
Intercept 260.8 2.353 256.2 265.4
Sex: Male 2.173 0.891 0.427 3.919
Focal haplotype: Brownsville -1.943 1.45 -4.784 0.899
Focal haplotype: Dahomey -2.688 1.417 -5.466 0.09
Focal haplotype: Israel -1.914 1.503 -4.86 1.031
Focal haplotype: Sweden -0.063 1.512 -3.025 2.9
Social haplotype: Brownsville 1.007 1.522 -1.977 3.991
Social haplotype: Dahomey -0.574 1.467 -3.45 2.302
Social haplotype: Israel 0.673 1.443 -2.156 3.502
Social haplotype: Sweden 1.376 1.479 -1.523 4.275

Adult fitness measures

Body size analysis


We use wing length as a proxy for adult body size.

It is distributed normally, so we fit a linear mixed model.

The model:

Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)

body_size_model <- lmer(Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Model evaluation

Table S3: Evaluation of the wing length model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors Duplicate strain, Block and Dyad ID. There was a clear top model; coefficients are displayed in Table S4.

# Compare all possible combinations of models (from the global model)

body_size_dredge <- dredge(body_size_model)

size_table <- subset(body_size_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()


names(size_table)[names(size_table) == "(Intercept)"] <- "Intercept"
names(size_table)[names(size_table) == "Focal_haplotype"] <- "Focal haplotype"
names(size_table)[names(size_table) == "Sex"] <- "Sex"
names(size_table)[names(size_table) == "Social_haplotype"] <- "Social haplotype"
names(size_table)[names(size_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(size_table)[names(size_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(size_table)[names(size_table) == "df"] <- "Degrees of freedom"
names(size_table)[names(size_table) == "logLik"] <- "Log likelihood"
names(size_table)[names(size_table) == "AICc"] <- "AICc"
names(size_table)[names(size_table) == "delta"] <- "Delta"
names(size_table)[names(size_table) == "weight"] <- "Weight"

pander(size_table, split.cell = 40, split.table = Inf)
  Intercept Focal haplotype Sex Social haplotype Focal haplotype x Sex Focal haplotype x Social haplotype Social haplotype x Sex Focal haplotype x Social haplotype x Sex Degrees of freedom Log likelihood AICc Delta Weight
3 1.063 NA + NA NA NA NA NA 6 440.6 -869 0 0.9201

Best fitting model

There is a clear top model; model avergaing is not required.

Model coefficients, standard error and 95% confidence limits listed in Table 3 are shown are shown for the wing length top model. Bold rows indicate significant effects.

# first get the RVI for each predictor

RVI_size <- MuMIn::sw(body_size_dredge)


# Fit the top model

body_size_model_final <- lmer(Wing_length ~ Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Size_CIs <- confint(body_size_model_final) %>%
  as.data.frame() %>% 
  slice(5:6)

Size_estimate <- coefTable(body_size_model_final) %>% as.data.frame()

Size_model_avg <- data.frame(Size_estimate, Size_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Size_model_avg) <- c("Intercept", "Sex: Male")

names(Size_model_avg)[names(Size_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Size_model_avg)[names(Size_model_avg) == "Std..Error"] <- "Standard Error"
names(Size_model_avg)[names(Size_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Size_model_avg)[names(Size_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Size_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = (2), round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval
Intercept 1.063 0.015 1.024 1.1
Sex: Male -0.086 0.007 -0.1 -0.072

Female reproductive output


To effectively accommodate zero-inflation, we modelled female offspring production using the glmmTMB package (Brooks et al. 2017). This package allows us to fit hurdle models and zero-inflated models.

Hurdle models treat zero-count and nonzero outcomes as two completely separate categories, while zero-inflated models treat zero-count outcomes as a mixture of structural and sampling zeros.

We analysed the number of offspring produced by females using a hurdle model with negative binomial errors. This approach allowed us to answer two questions: (1) did mtDNA and/or competition affect the incidence of failing to produce any offspring? and (2) for females that produced at least one offspring, was the number of offspring produced affected by mtDNA/competition?

The model:

Maternal_total_offspring ~ Focal_haplotype * Social_haplotype + (1|Strain) + (1|Block)

female_hurdle_model <- glmmTMB(Maternal_total_offspring ~ Social_haplotype * Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)

Model evaluation

Table S4: Evaluation of the female reproductive output model. All possible models were evaluated from the global model that included an interaction between Focal haplotype and Social haplotype and the random factors Strain and Block. As there was no clear top model, the final model was calculated via model averaging. The zero-inflated results correspond to question (1), while the conditional results correspond to question (2) above.

# Compare all possible combinations of models (from the global model)

if(file.exists("female_dredge.rds")){ # If already done, just load the results
  female_dredge <- readRDS("female_dredge.rds")
} else {female_dredge <- dredge(female_hurdle_model)                  # If not already done, run all the models and save the results
lapply(c("female_dredge"), save_it)
}


female_table <- subset(female_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(female_table)[names(female_table) == "cond((Int))"] <- "Conditional intercept"
names(female_table)[names(female_table) == "zi((Int))"] <- "Zero-inflated intercept"
names(female_table)[names(female_table) == "disp((Int))"] <- "Dispersion factor intercept"
names(female_table)[names(female_table) == "cond(Focal_haplotype)"] <- "Conditional (Focal haplotype)"
names(female_table)[names(female_table) == "cond(Social_haplotype)"] <- "Conditional (Social haplotype)"
names(female_table)[names(female_table) == "cond(Focal_haplotype:Social_haplotype)"] <- "Conditional (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype)"] <- "Zero-inflated (Focal haplotype)"
names(female_table)[names(female_table) == "zi(Social_haplotype)"] <- "Zero-inflated (Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype:Social_haplotype)"] <- "Zero-inflated (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "df"] <- "Degrees of freedom"
names(female_table)[names(female_table) == "logLik"] <- "Log likelihood"
names(female_table)[names(female_table) == "AICc"] <- "AICc"
names(female_table)[names(female_table) == "delta"] <- "Delta"
names(female_table)[names(female_table) == "weight"] <- "Weight"

pander(female_table, split.cell = 40, split.table = Inf)
  Conditional intercept Zero-inflated intercept Dispersion factor intercept Conditional (Focal haplotype) Conditional (Social haplotype) Conditional (Focal haplotype x Social haplotype) Zero-inflated (Focal haplotype) Zero-inflated (Social haplotype) Zero-inflated (Focal haplotype x Social haplotype) Degrees of freedom Log likelihood AICc Delta Weight
18 3.932 -1.245 + + NA NA NA + NA 15 -1546 3124 0 0.5844
17 3.839 -1.245 + NA NA NA NA + NA 11 -1552 3126 2.057 0.209
2 3.932 -0.783 + + NA NA NA NA NA 11 -1553 3128 4.256 0.06959
26 3.932 -1.156 + + NA NA + + NA 19 -1545 3129 4.796 0.05313

Model averaging

Zi (zero-hurdle requirement) and conditional (after hurdle) model coefficients, standard error and 95% confidence limits listed in Table 4 are shown for the female offspring production averaged model. Bold rows indicate significant effects.

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict()

top_female_models <- get.models(female_dredge, subset = delta < 6)

female_avgm <- model.avg(top_female_models)

# extract useful info

RVI_female <- MuMIn::sw(female_dredge)

Female_CIs <- confint(model.avg(female_dredge, subset = delta < 6)) %>% as.data.frame()

Female_estimate <- coefTable(model.avg(female_dredge, subset = delta < 6)) %>% as.data.frame()

Female_model_avg <- data.frame(Female_estimate, Female_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Female_model_avg) <- c("Conditional intercept", "Conditional focal haplotype: Brownsville", "Conditional focal haplotype: Dahomey", "Conditional focal haplotype: Israel", "Conditional focal haplotype: Sweden", "Zi intercept", "Zi social haplotype: Brownsville", "Zi social haplotype: Dahomey", "Zi social haplotype: Israel", "Zi social haplotype: Sweden", "Zi focal haplotype: Brownsville", "Zi focal haplotype: Dahomey", "Zi focal haplotype: Israel", "Zi focal haplotype: Sweden")


names(Female_model_avg)[names(Female_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Female_model_avg)[names(Female_model_avg) == "Std..Error"] <- "Standard Error"
names(Female_model_avg)[names(Female_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Female_model_avg)[names(Female_model_avg) == "X97.5.."] <- "97.5% Interval"

Female_model_avg %>%
  pander(split.cell = 40, split.table = Inf, emphasize.strong.rows = c(2, 5, 9), round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval
Conditional intercept 3.911 0.076 3.762 4.059
Conditional focal haplotype: Brownsville -0.191 0.08 -0.347 -0.035
Conditional focal haplotype: Dahomey -0.084 0.076 -0.234 0.065
Conditional focal haplotype: Israel -0.005 0.081 -0.163 0.152
Conditional focal haplotype: Sweden -0.213 0.083 -0.377 -0.049
Zi intercept -1.205 0.285 -1.764 -0.645
Zi social haplotype: Brownsville 0.552 0.359 -0.151 1.255
Zi social haplotype: Dahomey 0.188 0.353 -0.504 0.88
Zi social haplotype: Israel 1.051 0.335 0.395 1.707
Zi social haplotype: Sweden 0.371 0.361 -0.337 1.079
Zi focal haplotype: Brownsville 0.003 0.325 -0.635 0.641
Zi focal haplotype: Dahomey -0.28 0.333 -0.933 0.373
Zi focal haplotype: Israel 0.214 0.327 -0.427 0.855
Zi focal haplotype: Sweden -0.396 0.364 -1.109 0.318
# Plotting with model predictions

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype in the Zi formual. These were found by inspection of the top model list above.

focal_female_zi_models <- get.models(female_dredge, subset = "26")

# Note that only model "26' contains focal haplotype in the Zi formula. No averaging takes place and estimates are derived straight from this model. The conditional averaged estimates from the female_avgm object are identical to the estimates in model "26".

# fit model "26"

focal_zi_female_avg <- glmmTMB(Maternal_total_offspring ~ Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block), na.action = na.fail, REML = FALSE)


# Now average models that contain the social haplotype predictor in the Zi formula.

social_female_zi_models <- get.models(female_dredge, subset = c("18", "17", "26"))

social_zi_female_avg <- model.avg(social_female_zi_models)

# The conditional averaged estimates from the female_avgm object are identical to the zi social haplotype estimates from the "full model "social_zi_female_avg" object.

# Now average models that contain the focal haplotype predictor in the conditional formula.

focal_female_con_models <- get.models(female_dredge, subset = c("18", "2", "26"))

focal_con_female_avg <- model.avg(focal_female_con_models)

# Estimates match female_avg

# Make a new dataframe, for which we will derive predictions. It's the same as the old data, except that we set Focal haplotype, block and duplicate to the same value for all observations. The re.form = NA argument sets random effects to 0, meaning population means are calculated.
 
new_data <- female_reproductive_output %>%
  ungroup() %>%
  select(Focal_haplotype, Strain, Block) %>%
  mutate(Social_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# First lets get predictions for the average number of offspring produced by females that produced at least one progeny, split by focal haplotype.

pred <- predict(focal_con_female_avg, se.fit = TRUE, type = "conditional", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred1 <- pred %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred2 <- pred %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred <- cbind(new_data, pred1, pred2) %>%
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE) %>%
  rename(Maternal_total_offspring = mean_estimate)

# Load the data for each individual female that produced offspring so that this can be plotted

female_cond_plot_data <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0) %>%
  ungroup() %>% 
  select(Individual, Focal_haplotype, Maternal_total_offspring)

# Now lets plot these predictions

female_focal_cond_plot <- female_cond_plot_data %>%
  ggplot(aes(x = Focal_haplotype, y = Maternal_total_offspring, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = female_cond_plot_data, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
geom_point(data = pred, aes(x = Focal_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = pred, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now lets get the Zi predictions for focal haplotype

pred_ZI <- predict(focal_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_1 <- pred_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_2 <- pred_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI <- cbind(new_data, pred_ZI_1, pred_ZI_2) %>%
  transmute(Focal_haplotype, Strain, Block, Social_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
#rename(Maternal_total_offspring = mean_estimate)


# Plot
  
female_focal_zi_plot <- pred_focal_ZI %>%
  ggplot(aes(x = Focal_haplotype, y = mean_estimate, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_errorbar(aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Focal_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26', fill = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Female mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())
  

# Now create the newdata for social haplotype predictions

new_data_social <- female_reproductive_output %>%
  ungroup() %>%
  select(Social_haplotype, Strain, Block) %>%
  mutate(Focal_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# Get zi social haplotype predictions

pred_social_ZI <- predict(social_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data_social) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_social_1 <- pred_social_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_social_2 <- pred_social_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI_social <- cbind(new_data_social, pred_ZI_social_1, pred_ZI_social_2) %>% 
  transmute(Social_haplotype, Strain, Block, Focal_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
  # Plot 
  
female_social_zi_plot <- pred_focal_ZI_social %>%
  ggplot(aes(x = Social_haplotype, y = mean_estimate, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_errorbar(aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Social_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26', fill = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Male mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(female_focal_zi_plot, female_social_zi_plot, female_focal_cond_plot, labels = c("a", "b", "c"))

Figure 1: mtDNA directly and indirectly affects female fitness. Panels a and b show model predictions of the mean proportion of females that produced offspring (the zero-inflated or hurdle component of the model) across a female focal haplotypes and b social male haplotypes. Error bars depict standard errors. Panel c shows the direct effect of mtDNA on the number of offspring produced by a female. Black points show model predictions of the mean with standard error for each haplotype, while coloured points represent offspring produced by individual females.

Male adult fitness


Our measure of male fitness involves both pre- and post-copulatory competitive ability; that is we assess in one measure the combination of 1) the ability of a male to inseminate a female in the presence of another male and 2) the competitive ability of his sperm within females that have been inseminated by another male. Interestingly, the data contains many 0 or 1 values - corresponding to a monopoly of female fertilisation by one of the males. SOMETHING ABOUT THE BETA BINOMIAL DISTRIBUTION

We analyse male fitness as the proportion of offspring produced by mitochondrial strain males competing against a standard bw male competitor.

The Brownsville haplotype renders males sterile alongside the w1118 nuclear background and sub-fertile alongside all other tested backgrounds. In our experiment, we find that Brownsville males are able to produce offspring but to a very limited capacity. Due to this, our model is unable to produce reliable estimates when the interaction between focal and social haplotype is included. We do not include the interaction in the full model.

(Focal_male_offspring, bw_offspring) ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block) + (1|Individual)

response <- cbind(Male_fitness$Focal_male_offspring, Male_fitness$bw_male_offspring)

male_binary_model <- glmmTMB(response ~ Focal_haplotype + Social_haplotype + (1|Block) + (1|Strain) + (1|Individual), data = Male_fitness, family = "betabinomial", na.action = na.fail)

Model evaluation

Table S5: Evaluation of the male adult fitness model. All possible models were evaluated from the global model that included an interaction between focal haplotype and social haplotype and the random factors Strain, Block and Individual. As there was no clear top model, the final model was calculated via model averaging.

male_dredge <- dredge(male_binary_model)

Male_table <- subset(male_dredge, delta < 6) %>% as.data.frame()


names(Male_table)[names(Male_table) == "(Intercept)"] <- "Intercept"
names(Male_table)[names(Male_table) == "Focal_haplotype"] <- "Focal haplotype"
names(Male_table)[names(Male_table) == "Social_haplotype"] <- "Social haplotype"
names(Male_table)[names(Male_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(Male_table)[names(Male_table) == "df"] <- "Degrees of freedom"
names(Male_table)[names(Male_table) == "logLik"] <- "Log likelihood"
names(Male_table)[names(Male_table) == "AICc"] <- "AICc"
names(Male_table)[names(Male_table) == "delta"] <- "Delta"
names(Male_table)[names(Male_table) == "weight"] <- "Weight"

pander(Male_table, split.cell = 40, split.table = Inf)
  cond((Int)) disp((Int)) cond(Focal_haplotype) cond(Social_haplotype) Degrees of freedom Log likelihood AICc Delta Weight
2 -0.3087 + + NA 9 -835.2 1689 0 0.906
4 -0.1068 + + + 13 -833.2 1693 4.531 0.09401

Model averaging

Model coefficients, standard error and 95% confidence limits listed in Table 5 are shown for the male adult fitness averaged model. Bold rows indicate significant effects.

# Model average

top_male_models <- get.models(male_dredge, subset = delta < 6)

male_avgm <- model.avg(top_male_models)

# extract useful information

RVI_male <- MuMIn::sw(male_dredge)


# summary(model.avg(male_binary_dredge, subset = delta < 6))

Male_CIs <- confint(model.avg(male_dredge, subset = delta < 6)) %>% as.data.frame()

Male_estimate <- coefTable(model.avg(male_dredge, subset = delta < 6)) %>% as.data.frame()

Male_model_avg <- data.frame(Male_estimate, Male_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Male_model_avg) <- c("Intercept", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Male_model_avg)[names(Male_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Male_model_avg)[names(Male_model_avg) == "Std..Error"] <- "Standard Error"
names(Male_model_avg)[names(Male_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Male_model_avg)[names(Male_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Male_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)
  Conditional average estimate Standard Error 2.5% Interval 97.5% Interval
Intercept -0.29 0.276 -0.83 0.251
Focal haplotype: Brownsville -2.614 0.479 -3.552 -1.676
Focal haplotype: Dahomey -0.355 0.285 -0.914 0.205
Focal haplotype: Israel -0.075 0.314 -0.691 0.54
Focal haplotype: Sweden 0.143 0.303 -0.452 0.737
Social haplotype: Brownsville -0.546 0.321 -1.175 0.084
Social haplotype: Dahomey -0.065 0.326 -0.704 0.574
Social haplotype: Israel -0.233 0.316 -0.852 0.385
Social haplotype: Sweden -0.066 0.323 -0.699 0.568
# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype. These were found by inspection of the top model list above.

focal_male_models <- get.models(male_dredge, subset = c("2", "4"))

focal_male_avg <- model.avg(focal_male_models)

# Note that the conditional averaged estimates from the male_avgm object are identical to the full averaged estimates for the focal_male_avg object for focal haplotype.

# Now average models that contain the social haplotype predictor.

social_male_models <- get.models(male_dredge, subset = c("4"))

# Note that there is only one model (the original full model) that contains social haplotype in the < 6 delta subset, so estimates are calculated directly from this model - no averaging occurs. The conditional averaged estimates from the male_avgm object are identical to the estimates from the full model.



# Focal new data

new_data_male <- Male_fitness %>%
  ungroup() %>%
  select(Focal_haplotype, Block, Strain, Individual) %>%
  mutate(Social_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Individual = "4") %>% 
  distinct() 


pred_male_focal <- predict(focal_male_avg, newdata = new_data_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_focal_1 <- pred_male_focal %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_focal_2 <- pred_male_focal %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_focal_male <- cbind(new_data_male, pred_male_focal_1, pred_male_focal_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)

# Plot

Male_focal_plot <- Male_fitness %>%
  ggplot(aes(x = Focal_haplotype, y = Proportion_focal, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_focal_male, aes(x = Focal_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_focal_male, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Social new data

new_data_social_male <- Male_fitness %>%
  ungroup() %>%
  select(Social_haplotype, Block, Strain, Individual) %>%
  mutate(Focal_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Individual = "4") %>% 
  distinct()

# predict.averaging works over the full average rather than the conditional average that we present. I use a workaround where I create another model average object but only using the models in the < 6 delta subset that include social haplotype. Here only two models make the cut - the full model is the only one containing social haplotype as a predictor so no averaging is neccessary. Plug the full model into the predict function.

pred_male_social <- predict(male_binary_model, newdata = new_data_social_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_social_1 <- pred_male_social %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_social_2 <- pred_male_social %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_male_social <- cbind(new_data_social_male, pred_male_social_1, pred_male_social_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)
  

# Plot

Male_social_plot <- Male_fitness %>%
  ggplot(aes(x = Social_haplotype, y = Proportion_focal, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
 scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_male_social, aes(x = Social_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_male_social, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(Male_focal_plot, Male_social_plot, labels = c("a", "b"))

Figure 2: The proportion of offspring produced by mt-strain males competing with standard bw males. a shows the direct effect of mtDNA on male fitness. b shows the indirect genetic effect of female mtDNA on male fitness. Coloured points represent individual males, with larger points indicating a higher number of offspring produced in the vial (sired by either male). Black points show model predictions of the mean proportion of offspring sired by the mt-strain male, with associated standard errors.

Raw data and reproducibility

Table of raw data

For the purposes of completeness, transparency and data archiving, we include the raw data in this report.

Table S6: the raw data-set used in the present study, with NA values resulting from data collection mistakes removed (i.e. two females placed in competitive environment, no value recorded for whether the fly survived, flies that escaped during the experiment etc.).

kable(fitness_data %>% filter(!is.na(Survived)), "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "800px")
Individual Block Duplicate Dyad_environment Sex Focal_haplotype Social_haplotype Survived Social_survival Dev_time Day Hours Wing_length Maternal_female_offspring Maternal_male_offspring Maternal_total_offspring Paternal_focal_offspring Patneral_bw_offspring Proportion_focal
1 1 1 1 F Barcelona Barcelona 1 L NA NA NA NA 35 24 59 NA NA NA
2 1 1 1 M Barcelona Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
3 1 1 2 F Brownsville Barcelona 1 N 249 10 11 NA 36 45 81 NA NA NA
4 1 1 2 M Barcelona Brownsville 1 N NA NA NA NA NA NA NA 0 41 0.0000000
5 1 1 3 F Dahomey Barcelona 1 N 249 10 11 NA 17 23 40 NA NA NA
6 1 1 3 M Barcelona Dahomey 1 N 242 10 4 NA NA NA NA 42 0 1.0000000
7 1 1 4 F Israel Barcelona 1 N NA NA NA NA 0 0 0 NA NA NA
8 1 1 4 M Barcelona Israel 1 N 242 10 4 NA NA NA NA 32 26 0.5517241
9 1 1 5 F Sweden Barcelona 1 N NA NA NA NA 7 15 22 NA NA NA
10 1 1 5 M Barcelona Sweden 1 N 267 11 5 NA NA NA NA 0 0 0.0000000
11 1 1 6 F Barcelona Brownsville 0 P NA NA NA NA 0 0 0 NA NA NA
12 1 1 6 M Brownsville Barcelona 0 P NA NA NA NA NA NA NA 0 0 0.0000000
15 1 1 8 F Dahomey Brownsville 1 N 242 10 4 NA 23 31 54 NA NA NA
16 1 1 8 M Brownsville Dahomey 1 N 245 10 7 NA NA NA NA 0 63 0.0000000
17 1 1 9 F Israel Brownsville 1 N 243 10 5 NA NA NA NA NA NA NA
18 1 1 9 M Brownsville Israel 1 N NA NA NA NA NA NA NA 0 0 0.0000000
19 1 1 10 F Sweden Brownsville 1 L 266 11 4 NA 26 39 65 NA NA NA
20 1 1 10 M Brownsville Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
21 1 1 11 F Barcelona Dahomey 1 N NA NA NA NA 8 21 29 NA NA NA
22 1 1 11 M Dahomey Barcelona 1 N 267 11 5 NA NA NA NA 11 26 0.2972973
23 1 1 12 F Brownsville Dahomey 1 N 242 10 4 NA 11 22 33 NA NA NA
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640 3 1 320 M Israel Sweden 1 N NA NA NA NA NA NA NA 0 0 0.0000000
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679 3 1 340 F Sweden Dahomey 1 N 248 10 10 NA 0 0 0 NA NA NA
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691 3 1 346 F Barcelona Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
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701 3 1 351 F Barcelona Barcelona 1 P 262 10 0 NA NA NA NA NA NA NA
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710 3 1 355 M Barcelona Sweden 1 N 249 10 11 1.060 NA NA NA 71 13 0.8452381
711 3 1 356 F Barcelona Brownsville 1 N 267 11 5 1.074 38 35 73 NA NA NA
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721 3 1 361 F Barcelona Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
722 3 1 361 M Dahomey Barcelona 1 P 258 10 20 1.071 NA NA NA 151 1 0.9934211
723 3 1 362 F Brownsville Dahomey 1 N 255 10 17 1.178 5 5 10 NA NA NA
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728 3 1 364 M Dahomey Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
729 3 1 365 F Sweden Dahomey 1 N 266 11 4 NA 20 14 34 NA NA NA
730 3 1 365 M Dahomey Sweden 1 N 247 10 9 1.060 NA NA NA 112 30 0.7887324
731 3 1 366 F Barcelona Israel 1 N 260 10 22 1.179 0 0 0 NA NA NA
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736 3 1 368 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
737 3 1 369 F Israel Israel 0 N NA NA NA NA 0 0 0 NA NA NA
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739 3 1 370 F Sweden Israel 1 L 259 10 21 1.214 8 18 26 NA NA NA
740 3 1 370 M Israel Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
741 3 1 371 F Barcelona Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
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743 3 1 372 F Brownsville Sweden 1 N 263 10 1 1.057 31 21 52 NA NA NA
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749 3 1 375 F Sweden Sweden 1 P 268 11 6 1.140 38 31 69 NA NA NA
750 3 1 375 M Sweden Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
751 3 1 376 F Barcelona Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
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1777 5 2 889 F Israel Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1778 5 2 889 M Dahomey Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1779 5 2 890 F Sweden Dahomey 1 P 256 10 18 0.977 13 23 36 NA NA NA
1780 5 2 890 M Dahomey Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1781 5 2 891 F Barcelona Israel 1 L 272 11 10 1.056 0 0 0 NA NA NA
1782 5 2 891 M Israel Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1783 5 2 892 F Brownsville Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1784 5 2 892 M Israel Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1785 5 2 893 F Dahomey Israel 1 L 245 10 7 1.062 33 21 54 NA NA NA
1786 5 2 893 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1787 5 2 894 F Israel Israel 1 L 242 10 4 1.198 0 0 0 NA NA NA
1788 5 2 894 M Israel Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1789 5 2 895 F Sweden Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1790 5 2 895 M Israel Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1791 5 2 896 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1792 5 2 896 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1793 5 2 897 F Brownsville Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1794 5 2 897 M Sweden Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1795 5 2 898 F Dahomey Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1796 5 2 898 M Sweden Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1797 5 2 899 F Israel Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
1798 5 2 899 M Sweden Israel 1 L 250 10 12 NA NA NA NA 0 0 0.0000000
1799 5 2 900 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1800 5 2 900 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1801 6 1 901 F Barcelona Barcelona 1 L 252 10 14 NA 20 16 36 NA NA NA
1802 6 1 901 M Barcelona Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1803 6 1 902 F Brownsville Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1804 6 1 902 M Barcelona Brownsville 1 L 241 10 3 1.060 NA NA NA 36 68 0.3461538
1807 6 1 904 F Israel Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1808 6 1 904 M Barcelona Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1809 6 1 905 F Sweden Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1810 6 1 905 M Barcelona Sweden 1 L 260 10 22 1.084 NA NA NA 97 4 0.9603960
1811 6 1 906 F Barcelona Brownsville 1 L NA NA NA 1.177 31 35 66 NA NA NA
1812 6 1 906 M Brownsville Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1815 6 1 908 F Dahomey Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1816 6 1 908 M Brownsville Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1817 6 1 909 F Israel Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1818 6 1 909 M Brownsville Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1819 6 1 910 F Sweden Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1820 6 1 910 M Brownsville Sweden 1 L 257 10 19 0.898 NA NA NA 0 34 0.0000000
1821 6 1 911 F Barcelona Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1822 6 1 911 M Dahomey Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1823 6 1 912 F Brownsville Dahomey 1 N 242 10 4 1.018 17 18 35 NA NA NA
1824 6 1 912 M Dahomey Brownsville 1 N 260 10 22 0.932 NA NA NA 0 74 0.0000000
1825 6 1 913 F Dahomey Dahomey 0 P NA NA NA NA 0 0 0 NA NA NA
1826 6 1 913 M Dahomey Dahomey 0 P NA NA NA NA NA NA NA 0 0 0.0000000
1827 6 1 914 F Israel Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
1828 6 1 914 M Dahomey Israel 1 L 251 10 13 0.872 NA NA NA 0 35 0.0000000
1829 6 1 915 F Sweden Dahomey 1 L 226 9 12 1.096 30 37 67 NA NA NA
1830 6 1 915 M Dahomey Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1831 6 1 916 F Barcelona Israel 0 N NA NA NA NA 0 0 0 NA NA NA
1832 6 1 916 M Israel Barcelona 1 L 270 11 8 0.865 NA NA NA 0 101 0.0000000
1833 6 1 917 F Brownsville Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1834 6 1 917 M Israel Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1835 6 1 918 F Dahomey Israel 1 L 259 10 21 0.959 0 0 0 NA NA NA
1836 6 1 918 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1837 6 1 919 F Israel Israel 0 P NA NA NA NA 0 0 0 NA NA NA
1838 6 1 919 M Israel Israel 0 P NA NA NA NA NA NA NA 0 0 0.0000000
1839 6 1 920 F Sweden Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1840 6 1 920 M Israel Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1843 6 1 922 F Brownsville Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1844 6 1 922 M Sweden Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1845 6 1 923 F Dahomey Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1846 6 1 923 M Sweden Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1847 6 1 924 F Israel Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1848 6 1 924 M Sweden Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1849 6 1 925 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1850 6 1 925 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1851 6 1 926 F Barcelona Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1852 6 1 926 M Barcelona Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1853 6 1 927 F Brownsville Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1854 6 1 927 M Barcelona Brownsville 1 L 269 11 7 NA NA NA NA 0 0 0.0000000
1855 6 1 928 F Dahomey Barcelona 1 L 240 10 2 1.100 30 28 58 NA NA NA
1856 6 1 928 M Barcelona Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1857 6 1 929 F Israel Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1858 6 1 929 M Barcelona Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1859 6 1 930 F Sweden Barcelona 1 N 252 10 14 NA 22 17 39 NA NA NA
1860 6 1 930 M Barcelona Sweden 1 N 241 10 3 NA NA NA NA 0 107 0.0000000
1861 6 1 931 F Barcelona Brownsville 1 L 246 10 8 NA 0 0 0 NA NA NA
1862 6 1 931 M Brownsville Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1863 6 1 932 F Brownsville Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1864 6 1 932 M Brownsville Brownsville 1 L 230 9 16 NA NA NA NA 0 0 0.0000000
1865 6 1 933 F Dahomey Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1866 6 1 933 M Brownsville Dahomey 1 L 247 10 9 NA NA NA NA 0 0 0.0000000
1867 6 1 934 F Israel Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1868 6 1 934 M Brownsville Israel 1 L 261 10 23 0.892 NA NA NA 0 56 0.0000000
1869 6 1 935 F Sweden Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1870 6 1 935 M Brownsville Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1871 6 1 936 F Barcelona Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1872 6 1 936 M Dahomey Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1873 6 1 937 F Brownsville Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1874 6 1 937 M Dahomey Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1875 6 1 938 F Dahomey Dahomey 1 N 256 10 18 0.976 0 0 0 NA NA NA
1876 6 1 938 M Dahomey Dahomey 1 N 247 10 9 NA NA NA NA 0 28 0.0000000
1877 6 1 939 F Israel Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1878 6 1 939 M Dahomey Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1879 6 1 940 F Sweden Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1880 6 1 940 M Dahomey Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1881 6 1 941 F Barcelona Israel 1 L 247 10 9 NA 0 0 0 NA NA NA
1882 6 1 941 M Israel Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1883 6 1 942 F Brownsville Israel 0 N NA NA NA NA 0 0 0 NA NA NA
1884 6 1 942 M Israel Brownsville 1 P 257 10 19 0.918 NA NA NA 0 105 0.0000000
1885 6 1 943 F Dahomey Israel 1 L 254 10 16 NA 0 0 0 NA NA NA
1886 6 1 943 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1887 6 1 944 F Israel Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1888 6 1 944 M Israel Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1889 6 1 945 F Sweden Israel 1 N 247 10 9 NA 0 0 0 NA NA NA
1890 6 1 945 M Israel Sweden 1 N 239 9 1 NA NA NA NA 0 65 0.0000000
1891 6 1 946 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1892 6 1 946 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1893 6 1 947 F Brownsville Sweden 1 L 239 9 1 NA 0 0 0 NA NA NA
1894 6 1 947 M Sweden Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1895 6 1 948 F Dahomey Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1896 6 1 948 M Sweden Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1897 6 1 949 F Israel Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1898 6 1 949 M Sweden Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1899 6 1 950 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1900 6 1 950 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1901 6 1 951 F Barcelona Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1902 6 1 951 M Barcelona Barcelona 1 P 273 11 11 0.865 NA NA NA 0 0 0.0000000
1903 6 1 952 F Brownsville Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1904 6 1 952 M Barcelona Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1905 6 1 953 F Dahomey Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1906 6 1 953 M Barcelona Dahomey 1 L 254 10 16 0.929 NA NA NA 0 35 0.0000000
1907 6 1 954 F Israel Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1908 6 1 954 M Barcelona Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1909 6 1 955 F Sweden Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1910 6 1 955 M Barcelona Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1911 6 1 956 F Barcelona Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1912 6 1 956 M Brownsville Barcelona 1 P 247 10 9 NA NA NA NA 0 48 0.0000000
1913 6 1 957 F Brownsville Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1914 6 1 957 M Brownsville Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1915 6 1 958 F Dahomey Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1916 6 1 958 M Brownsville Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1917 6 1 959 F Israel Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1918 6 1 959 M Brownsville Israel 1 L 244 10 6 0.706 NA NA NA 0 118 0.0000000
1919 6 1 960 F Sweden Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
1920 6 1 960 M Brownsville Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1921 6 1 961 F Barcelona Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1922 6 1 961 M Dahomey Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1923 6 1 962 F Brownsville Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1924 6 1 962 M Dahomey Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1925 6 1 963 F Dahomey Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1926 6 1 963 M Dahomey Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1927 6 1 964 F Israel Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
1928 6 1 964 M Dahomey Israel 1 L 249 10 11 1.078 NA NA NA 84 22 0.7924528
1929 6 1 965 F Sweden Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
1930 6 1 965 M Dahomey Sweden 1 L 243 10 5 1.008 NA NA NA 49 6 0.8909091
1931 6 1 966 F Barcelona Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1932 6 1 966 M Israel Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1933 6 1 967 F Brownsville Israel 1 P NA NA NA 1.094 0 0 0 NA NA NA
1934 6 1 967 M Israel Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1935 6 1 968 F Dahomey Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1936 6 1 968 M Israel Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1937 6 1 969 F Israel Israel 1 N 270 11 8 NA 0 0 0 NA NA NA
1938 6 1 969 M Israel Israel 1 N 268 11 6 0.844 NA NA NA 73 36 0.6697248
1939 6 1 970 F Sweden Israel 1 N 253 10 15 1.068 28 18 46 NA NA NA
1940 6 1 970 M Israel Sweden 1 N 271 11 9 0.900 NA NA NA 39 16 0.7090909
1941 6 1 971 F Barcelona Sweden 1 P 245 10 7 1.040 21 25 46 NA NA NA
1942 6 1 971 M Sweden Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1943 6 1 972 F Brownsville Sweden 1 L 241 10 3 NA 0 0 0 NA NA NA
1944 6 1 972 M Sweden Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1945 6 1 973 F Dahomey Sweden 1 P 251 10 13 NA 1 1 2 NA NA NA
1946 6 1 973 M Sweden Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1951 6 1 976 F Barcelona Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1952 6 1 976 M Barcelona Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1953 6 1 977 F Brownsville Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1954 6 1 977 M Barcelona Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1955 6 1 978 F Dahomey Barcelona 1 P 257 10 19 0.985 22 32 54 NA NA NA
1956 6 1 978 M Barcelona Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1957 6 1 979 F Israel Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
1958 6 1 979 M Barcelona Israel 1 P 277 11 15 NA NA NA NA 0 66 0.0000000
1959 6 1 980 F Sweden Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
1960 6 1 980 M Barcelona Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1961 6 1 981 F Barcelona Brownsville 1 N 255 10 17 0.989 8 8 16 NA NA NA
1962 6 1 981 M Brownsville Barcelona 1 N 287 12 1 0.919 NA NA NA 0 66 0.0000000
1963 6 1 982 F Brownsville Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1964 6 1 982 M Brownsville Brownsville 1 L 247 10 9 0.999 NA NA NA 0 90 0.0000000
1965 6 1 983 F Dahomey Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1966 6 1 983 M Brownsville Dahomey 1 L NA NA NA 0.910 NA NA NA 0 14 0.0000000
1967 6 1 984 F Israel Brownsville 0 P NA NA NA NA 0 0 0 NA NA NA
1968 6 1 984 M Brownsville Israel 0 P NA NA NA NA NA NA NA 0 0 0.0000000
1969 6 1 985 F Sweden Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
1970 6 1 985 M Brownsville Sweden 1 P 240 10 2 0.999 NA NA NA 0 0 0.0000000
1971 6 1 986 F Barcelona Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1972 6 1 986 M Dahomey Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1973 6 1 987 F Brownsville Dahomey 1 P 242 10 4 1.024 29 30 59 NA NA NA
1974 6 1 987 M Dahomey Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1975 6 1 988 F Dahomey Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1976 6 1 988 M Dahomey Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1977 6 1 989 F Israel Dahomey 1 L 242 10 4 NA 0 0 0 NA NA NA
1978 6 1 989 M Dahomey Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1979 6 1 990 F Sweden Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
1980 6 1 990 M Dahomey Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1981 6 1 991 F Barcelona Israel 0 L NA NA NA NA 0 0 0 NA NA NA
1982 6 1 991 M Israel Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1983 6 1 992 F Brownsville Israel 1 P 245 10 7 0.996 23 18 41 NA NA NA
1984 6 1 992 M Israel Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1985 6 1 993 F Dahomey Israel 1 L 250 10 12 NA 0 0 0 NA NA NA
1986 6 1 993 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1987 6 1 994 F Israel Israel 1 L 272 11 10 0.994 2 4 6 NA NA NA
1988 6 1 994 M Israel Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1989 6 1 995 F Sweden Israel 0 N NA NA NA NA 0 0 0 NA NA NA
1990 6 1 995 M Israel Sweden 1 L 243 10 5 1.012 NA NA NA 0 4 0.0000000
1991 6 1 996 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
1992 6 1 996 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
1993 6 1 997 F Brownsville Sweden 1 N 242 10 4 1.033 26 28 54 NA NA NA
1994 6 1 997 M Sweden Brownsville 1 N 257 10 19 0.906 NA NA NA 48 60 0.4444444
1995 6 1 998 F Dahomey Sweden 1 L 275 11 13 0.908 0 0 0 NA NA NA
1996 6 1 998 M Sweden Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
1997 6 1 999 F Israel Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
1998 6 1 999 M Sweden Israel 1 L 248 10 10 0.987 NA NA NA 124 23 0.8435374
1999 6 1 1000 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2000 6 1 1000 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2001 6 1 1001 F Barcelona Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
2002 6 1 1001 M Barcelona Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2003 6 1 1002 F Brownsville Barcelona 1 N 269 11 7 1.042 23 15 38 NA NA NA
2004 6 1 1002 M Barcelona Brownsville 1 N 271 11 9 0.963 NA NA NA 97 24 0.8016529
2005 6 1 1003 F Dahomey Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2006 6 1 1003 M Barcelona Dahomey 1 P 244 10 6 1.059 NA NA NA 58 9 0.8656716
2007 6 1 1004 F Israel Barcelona 1 P 250 10 12 NA 27 21 48 NA NA NA
2008 6 1 1004 M Barcelona Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2009 6 1 1005 F Sweden Barcelona 1 P 248 10 10 1.149 23 20 43 NA NA NA
2010 6 1 1005 M Barcelona Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2011 6 1 1006 F Barcelona Brownsville 1 N 274 11 12 NA 0 0 0 NA NA NA
2012 6 1 1006 M Brownsville Barcelona 1 N 242 10 4 1.030 NA NA NA 0 32 0.0000000
2013 6 1 1007 F Brownsville Brownsville 1 N 254 10 16 NA 0 0 0 NA NA NA
2014 6 1 1007 M Brownsville Brownsville 1 N 243 10 5 0.947 NA NA NA 0 0 0.0000000
2015 6 1 1008 F Dahomey Brownsville 1 L 253 10 15 1.157 48 26 74 NA NA NA
2016 6 1 1008 M Brownsville Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2017 6 1 1009 F Israel Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2018 6 1 1009 M Brownsville Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2019 6 1 1010 F Sweden Brownsville 1 N 266 11 4 0.998 1 2 3 NA NA NA
2020 6 1 1010 M Brownsville Sweden 1 N 288 12 2 NA NA NA NA 0 0 0.0000000
2021 6 1 1011 F Barcelona Dahomey 1 N 247 10 9 1.092 16 26 42 NA NA NA
2022 6 1 1011 M Dahomey Barcelona 1 N 250 10 12 1.060 NA NA NA 45 3 0.9375000
2023 6 1 1012 F Brownsville Dahomey 1 N 272 11 10 1.005 14 21 35 NA NA NA
2024 6 1 1012 M Dahomey Brownsville 1 N 284 11 22 1.021 NA NA NA 4 0 1.0000000
2025 6 1 1013 F Dahomey Dahomey 1 P 245 10 7 1.074 38 32 70 NA NA NA
2026 6 1 1013 M Dahomey Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2027 6 1 1014 F Israel Dahomey 1 N 274 11 12 0.967 0 0 0 NA NA NA
2028 6 1 1014 M Dahomey Israel 1 N 241 10 3 1.042 NA NA NA 0 54 0.0000000
2029 6 1 1015 F Sweden Dahomey 1 L 240 10 2 NA 33 31 64 NA NA NA
2030 6 1 1015 M Dahomey Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2031 6 1 1016 F Barcelona Israel 1 P 248 10 10 1.106 22 30 52 NA NA NA
2032 6 1 1016 M Israel Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2033 6 1 1017 F Brownsville Israel 1 N 245 10 7 1.063 7 9 16 NA NA NA
2034 6 1 1017 M Israel Brownsville 1 N 272 11 10 0.876 NA NA NA 0 96 0.0000000
2035 6 1 1018 F Dahomey Israel 1 N 252 10 14 0.985 18 21 39 NA NA NA
2036 6 1 1018 M Israel Dahomey 1 N 263 10 1 0.904 NA NA NA 0 58 0.0000000
2037 6 1 1019 F Israel Israel 1 L 231 9 17 1.053 28 25 53 NA NA NA
2038 6 1 1019 M Israel Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2039 6 1 1020 F Sweden Israel 1 N 238 9 0 1.205 0 0 0 NA NA NA
2040 6 1 1020 M Israel Sweden 1 N 247 10 9 NA NA NA NA NA NA NA
2041 6 1 1021 F Barcelona Sweden 1 L 273 11 11 1.106 31 38 69 NA NA NA
2042 6 1 1021 M Sweden Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2043 6 1 1022 F Brownsville Sweden 1 L 275 11 13 NA 0 0 0 NA NA NA
2044 6 1 1022 M Sweden Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2045 6 1 1023 F Dahomey Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2046 6 1 1023 M Sweden Dahomey 1 L 252 10 14 NA NA NA NA 0 19 0.0000000
2049 6 1 1025 F Sweden Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2050 6 1 1025 M Sweden Sweden 1 L 268 11 6 NA NA NA NA 0 0 0.0000000
2051 6 1 1026 F Barcelona Barcelona 1 N 257 10 19 1.090 21 26 47 NA NA NA
2052 6 1 1026 M Barcelona Barcelona 1 N 255 10 17 0.995 NA NA NA 90 0 1.0000000
2053 6 1 1027 F Brownsville Barcelona 1 N 270 11 8 NA 0 0 0 NA NA NA
2054 6 1 1027 M Barcelona Brownsville 1 N 255 10 17 0.995 NA NA NA 0 16 0.0000000
2055 6 1 1028 F Dahomey Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2056 6 1 1028 M Barcelona Dahomey 1 L 255 10 17 0.973 NA NA NA 70 68 0.5072464
2057 6 1 1029 F Israel Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2058 6 1 1029 M Barcelona Israel 1 L 275 11 13 NA NA NA NA 0 0 0.0000000
2059 6 1 1030 F Sweden Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2060 6 1 1030 M Barcelona Sweden 1 L 268 11 6 0.965 NA NA NA 81 33 0.7105263
2063 6 1 1032 F Brownsville Brownsville 1 L 245 10 7 1.165 39 34 73 NA NA NA
2064 6 1 1032 M Brownsville Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2065 6 1 1033 F Dahomey Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2066 6 1 1033 M Brownsville Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2067 6 1 1034 F Israel Brownsville 1 N 250 10 12 1.070 24 25 49 NA NA NA
2068 6 1 1034 M Brownsville Israel 1 N 254 10 16 NA NA NA NA 0 99 0.0000000
2069 6 1 1035 F Sweden Brownsville 1 N 258 10 20 0.994 0 0 0 NA NA NA
2070 6 1 1035 M Brownsville Sweden 1 N 274 11 12 0.902 NA NA NA 0 43 0.0000000
2071 6 1 1036 F Barcelona Dahomey 1 N 241 10 3 1.103 22 23 45 NA NA NA
2072 6 1 1036 M Dahomey Barcelona 1 N 254 10 16 0.981 NA NA NA 14 29 0.3255814
2073 6 1 1037 F Brownsville Dahomey 1 N 230 9 16 0.916 20 20 40 NA NA NA
2074 6 1 1037 M Dahomey Brownsville 1 N 242 10 4 0.995 NA NA NA 0 17 0.0000000
2075 6 1 1038 F Dahomey Dahomey 1 L 252 10 14 1.102 27 28 55 NA NA NA
2076 6 1 1038 M Dahomey Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2077 6 1 1039 F Israel Dahomey 1 N 259 10 21 1.076 19 18 37 NA NA NA
2078 6 1 1039 M Dahomey Israel 1 N 275 11 13 0.944 NA NA NA 121 0 1.0000000
2079 6 1 1040 F Sweden Dahomey 1 L 269 11 7 1.003 24 23 47 NA NA NA
2080 6 1 1040 M Dahomey Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2081 6 1 1041 F Barcelona Israel 1 L 245 10 7 1.212 0 0 0 NA NA NA
2082 6 1 1041 M Israel Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2083 6 1 1042 F Brownsville Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2084 6 1 1042 M Israel Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2087 6 1 1044 F Israel Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2088 6 1 1044 M Israel Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2089 6 1 1045 F Sweden Israel 1 L 276 11 14 0.940 6 13 19 NA NA NA
2090 6 1 1045 M Israel Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2091 6 1 1046 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2092 6 1 1046 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2093 6 1 1047 F Brownsville Sweden 1 L 254 10 16 0.897 8 14 22 NA NA NA
2094 6 1 1047 M Sweden Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2095 6 1 1048 F Dahomey Sweden 1 N 252 10 14 1.011 17 27 44 NA NA NA
2096 6 1 1048 M Sweden Dahomey 1 N 262 10 0 0.970 NA NA NA 106 8 0.9298246
2097 6 1 1049 F Israel Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2098 6 1 1049 M Sweden Israel 1 L 286 12 0 NA NA NA NA 0 0 0.0000000
2099 6 1 1050 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2100 6 1 1050 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2101 6 1 1051 F Barcelona Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
2102 6 1 1051 M Barcelona Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2103 6 1 1052 F Brownsville Barcelona 1 N 252 10 14 1.085 27 28 55 NA NA NA
2104 6 1 1052 M Barcelona Brownsville 1 N 277 11 15 0.917 NA NA NA 0 82 0.0000000
2105 6 1 1053 F Dahomey Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
2106 6 1 1053 M Barcelona Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2107 6 1 1054 F Israel Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
2108 6 1 1054 M Barcelona Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2109 6 1 1055 F Sweden Barcelona 1 P 259 10 21 0.936 10 9 19 NA NA NA
2110 6 1 1055 M Barcelona Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2111 6 1 1056 F Barcelona Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2112 6 1 1056 M Brownsville Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2113 6 1 1057 F Brownsville Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2114 6 1 1057 M Brownsville Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2115 6 1 1058 F Dahomey Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2116 6 1 1058 M Brownsville Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2117 6 1 1059 F Israel Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2118 6 1 1059 M Brownsville Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2119 6 1 1060 F Sweden Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2120 6 1 1060 M Brownsville Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2121 6 1 1061 F Barcelona Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2122 6 1 1061 M Dahomey Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2123 6 1 1062 F Brownsville Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2124 6 1 1062 M Dahomey Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2125 6 1 1063 F Dahomey Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2126 6 1 1063 M Dahomey Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2127 6 1 1064 F Israel Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2128 6 1 1064 M Dahomey Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2129 6 1 1065 F Sweden Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2130 6 1 1065 M Dahomey Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2131 6 1 1066 F Barcelona Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2132 6 1 1066 M Israel Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2133 6 1 1067 F Brownsville Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2134 6 1 1067 M Israel Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2135 6 1 1068 F Dahomey Israel 1 N 256 10 18 0.999 28 18 46 NA NA NA
2136 6 1 1068 M Israel Dahomey 1 N 275 11 13 0.827 NA NA NA 0 30 0.0000000
2137 6 1 1069 F Israel Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2138 6 1 1069 M Israel Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2139 6 1 1070 F Sweden Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2140 6 1 1070 M Israel Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2141 6 1 1071 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2142 6 1 1071 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2143 6 1 1072 F Brownsville Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2144 6 1 1072 M Sweden Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2145 6 1 1073 F Dahomey Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2146 6 1 1073 M Sweden Dahomey 1 P 274 11 12 NA NA NA NA 0 49 0.0000000
2147 6 1 1074 F Israel Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2148 6 1 1074 M Sweden Israel 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2149 6 1 1075 F Sweden Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2150 6 1 1075 M Sweden Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2151 6 1 1076 F Barcelona Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2152 6 1 1076 M Barcelona Barcelona 1 L 244 10 6 NA NA NA NA 0 0 0.0000000
2153 6 1 1077 F Brownsville Barcelona 0 L NA NA NA NA 0 0 0 NA NA NA
2154 6 1 1077 M Barcelona Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2155 6 1 1078 F Dahomey Barcelona 1 N 256 10 18 1.080 13 4 17 NA NA NA
2156 6 1 1078 M Barcelona Dahomey 1 N 245 10 7 1.017 NA NA NA 46 38 0.5476190
2157 6 1 1079 F Israel Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2158 6 1 1079 M Barcelona Israel 1 L 243 10 5 1.059 NA NA NA 50 2 0.9615385
2159 6 1 1080 F Sweden Barcelona 0 N NA NA NA NA 0 0 0 NA NA NA
2160 6 1 1080 M Barcelona Sweden 1 P 261 10 23 0.952 NA NA NA 107 53 0.6687500
2161 6 1 1081 F Barcelona Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2162 6 1 1081 M Brownsville Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2163 6 1 1082 F Brownsville Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2164 6 1 1082 M Brownsville Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2165 6 1 1083 F Dahomey Brownsville 0 L NA NA NA NA 0 0 0 NA NA NA
2166 6 1 1083 M Brownsville Dahomey 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2167 6 1 1084 F Israel Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
2168 6 1 1084 M Brownsville Israel 1 P 269 11 7 NA NA NA NA 0 79 0.0000000
2169 6 1 1085 F Sweden Brownsville 0 N NA NA NA NA 0 0 0 NA NA NA
2170 6 1 1085 M Brownsville Sweden 1 P 260 10 22 0.993 NA NA NA 0 45 0.0000000
2171 6 1 1086 F Barcelona Dahomey 1 L 258 10 20 1.057 25 28 53 NA NA NA
2172 6 1 1086 M Dahomey Barcelona 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2173 6 1 1087 F Brownsville Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2174 6 1 1087 M Dahomey Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2175 6 1 1088 F Dahomey Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
2176 6 1 1088 M Dahomey Dahomey 1 L NA NA NA NA NA NA NA 0 0 0.0000000
2177 6 1 1089 F Israel Dahomey 0 N NA NA NA NA 0 0 0 NA NA NA
2178 6 1 1089 M Dahomey Israel 1 L 262 10 0 0.988 NA NA NA 0 48 0.0000000
2179 6 1 1090 F Sweden Dahomey 0 L NA NA NA NA 0 0 0 NA NA NA
2180 6 1 1090 M Dahomey Sweden 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2181 6 1 1091 F Barcelona Israel 0 N NA NA NA NA 0 0 0 NA NA NA
2182 6 1 1091 M Israel Barcelona 1 P 257 10 19 NA NA NA NA NA NA NA
2183 6 1 1092 F Brownsville Israel 0 L NA NA NA NA 0 0 0 NA NA NA
2184 6 1 1092 M Israel Brownsville 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2185 6 1 1093 F Dahomey Israel 1 P 275 11 13 NA 13 18 31 NA NA NA
2186 6 1 1093 M Israel Dahomey 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2187 6 1 1094 F Israel Israel 1 P 245 10 7 1.100 19 24 43 NA NA NA
2188 6 1 1094 M Israel Israel 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2189 6 1 1095 F Sweden Israel 1 L 251 10 13 1.124 21 45 66 NA NA NA
2190 6 1 1095 M Israel Sweden 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2191 6 1 1096 F Barcelona Sweden 0 L NA NA NA NA 0 0 0 NA NA NA
2192 6 1 1096 M Sweden Barcelona 0 L NA NA NA NA NA NA NA 0 0 0.0000000
2193 6 1 1097 F Brownsville Sweden 1 L 275 11 13 NA 0 0 0 NA NA NA
2194 6 1 1097 M Sweden Brownsville 0 N NA NA NA NA NA NA NA 0 0 0.0000000
2195 6 1 1098 F Dahomey Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2196 6 1 1098 M Sweden Dahomey 1 L 253 10 15 1.071 NA NA NA 58 81 0.4172662
2197 6 1 1099 F Israel Sweden 1 N NA NA NA 1.114 9 14 23 NA NA NA
2198 6 1 1099 M Sweden Israel 1 N 278 11 16 NA NA NA NA 0 0 0.0000000
2199 6 1 1100 F Sweden Sweden 0 N NA NA NA NA 0 0 0 NA NA NA
2200 6 1 1100 M Sweden Sweden 1 P 274 11 12 NA NA NA NA 0 0 0.0000000

Columns represent:

Individual: the focal fly being tested.

Block: the distinct period of time that the particular individual was tested.

Strain: Which of the 10 combinations of haplotype and duplicate strain was the individual from?

Replicate: A set of all possible combinations of the 5 haplotypes. Each replicate contained 25 cells.

Sex: was the focal individual female or male?

Focal_haplotype: what mtDNA haplotype did the focal individual carry?

Social_haplotype: what mtDNA haplotype did the social competitor of the focal individual carry?

Survived: did the focal individual survive to adulthood (1) or die during larval development (0)?

Social_survival: did the social competitor die as a larva (L), as a pupa (P) or survive to adulthood (N)?

Dev_time: how many hours did it take for the focal individual to progress from an egg to an adult. NA values indicate where individuals did not survive or development time could not be measured.

Day: how many days did it take the focal individual to develop?

Hours: lights were turned on at 7am every morning. How many hours did it take for individuals to eclose after this time?

Wing_length: what was the length in mm of the focal individuals right wing?

Maternal_female_offspring: how many adult female offspring did a focal female produce in a two day period?

Maternal_male_offspring: how many adult male offspring did a focal female produce in a two day period?

Maternal_total_offspring: how many adult offspring did a focal female produce in a two day period?

Paternal_focal_offspring: how many red-eye phenotype offspring were there across the two vials in the male adult fitness assay?

Paternal_bw_offspring: how many brown-eye phenotype offspring were there across the two vials in the male adult fitness assay?

R session information

This section provides information on the operating system and R packages attached during the production of this document, to allow easier replication of the analysis.

sessionInfo() %>% pander

R version 3.6.1 (2019-07-05)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: groupdata2(v.1.1.2), stringr(v.1.4.0), pander(v.0.6.3), kableExtra(v.1.1.0), ggResidpanel(v.0.3.0), ggbeeswarm(v.0.6.0), ggpubr(v.0.2.2), magrittr(v.1.5), ggridges(v.0.5.1), ggplot2(v.3.2.1), MuMIn(v.1.43.6), dplyr(v.0.8.3), car(v.3.0-3), carData(v.3.0-2), glmmTMB(v.0.2.3), lmerTest(v.3.1-0), lme4(v.1.1-21) and Matrix(v.1.2-17)

loaded via a namespace (and not attached): httr(v.1.4.1), tidyr(v.0.8.3), jsonlite(v.1.6), viridisLite(v.0.3.0), splines(v.3.6.1), assertthat(v.0.2.1), highr(v.0.8), stats4(v.3.6.1), vipor(v.0.4.5), cellranger(v.1.1.0), yaml(v.2.2.0), robustbase(v.0.93-5), numDeriv(v.2016.8-1.1), pillar(v.1.4.2), backports(v.1.1.4), lattice(v.0.20-38), glue(v.1.3.1), digest(v.0.6.20), ggsignif(v.0.6.0), rvest(v.0.3.4), minqa(v.1.2.4), colorspace(v.1.4-1), cowplot(v.1.0.0), htmltools(v.0.3.6), plyr(v.1.8.4), pkgconfig(v.2.0.2), haven(v.2.1.1), purrr(v.0.3.2), webshot(v.0.5.1), scales(v.1.0.0), openxlsx(v.4.1.0.1), rio(v.0.5.16), tibble(v.2.1.3), withr(v.2.1.2), TMB(v.1.7.15), lazyeval(v.0.2.2), crayon(v.1.3.4), readxl(v.1.3.1), evaluate(v.0.14), nlme(v.3.1-140), MASS(v.7.3-51.4), xml2(v.1.2.2), forcats(v.0.4.0), foreign(v.0.8-71), beeswarm(v.0.2.3), tools(v.3.6.1), data.table(v.1.12.2), hms(v.0.5.0), plotly(v.4.9.0), munsell(v.0.5.0), zip(v.2.0.3), qqplotr(v.0.0.3), compiler(v.3.6.1), rlang(v.0.4.0), grid(v.3.6.1), nloptr(v.1.2.1), rstudioapi(v.0.10), htmlwidgets(v.1.3), labeling(v.0.3), base64enc(v.0.1-3), rmarkdown(v.1.14), boot(v.1.3-22), codetools(v.0.2-16), gtable(v.0.3.0), abind(v.1.4-5), curl(v.4.0), R6(v.2.4.0), knitr(v.1.24), zeallot(v.0.1.0), readr(v.1.3.1), stringi(v.1.4.3), Rcpp(v.1.0.2), vctrs(v.0.2.0), DEoptimR(v.1.0-8), tidyselect(v.0.2.5) and xfun(v.0.8)

References

Brooks, Mollie E, Kasper Kristensen, Koen J van Benthem, Arni Magnusson, Casper W Berg, Anders Nielsen, Hans J Skaug, Martin Maechler, and Benjamin M Bolker. 2017. “Modeling Zero-Inflated Count Data with glmmTMB.” Journal Article. BioRxiv, 132753.

Symonds, Matthew R. E., and Adnan Moussalli. 2011. “A Brief Guide to Model Selection, Multimodel Inference and Model Averaging in Behavioural Ecology Using Akaike’s Information Criterion.” Journal Article. Behavioral Ecology and Sociobiology 65 (1): 13–21. https://doi.org/10.1007/s00265-010-1037-6.

---
title: Kin selection subverts mitochondrial transmission bias and allows male mtDNA evolution
author: "Thomas Keaney, Heidi Wong, Damian Dowling, Theresa Jones, and Luke Holman"
bibliography: "supp_references.bib"
subtitle: Supplementary material
output:
  html_document:
    code_folding: hide
    depth: 1
    number_sections: no
    theme: yeti
    toc: yes
    toc_float: yes
    code_download: true
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE, warning = FALSE, message = FALSE)
```

# Supplementary methods


Schematics for replacement of the unknown _Sxl-GFP_ nuclear background with the isogenic _w^1118^_ background

![**Figure S1**: Crossing scheme used to create a standard homozygous GFP-w line. Males from this line were crossed with females carrying a specific mitochondrial haplotype, to create experimental mitolines. These newly produced lines carried the mitochondrial haplotype of the female and were heterozygous for the Sxl-GFP construct. G1 = the first generation of the cross.](Crossing_scheme.png)


# Data analysis and supplementary results


Here we include all code used to run our analysis, our rationale behind the modelling approaches, and all remaining supplementary tables and figures.

#### Load packages, read in the data and create some helpful functions

**EDIT THE PACKAGE LIST WHEN HAPPY WITH THE ANALYSIS**

```{r packages data and functions}

# load relevant packages

library(lme4) # for the lmer and glmer mixed model functions
library(lmerTest) # Used to get p-values for lmer models using simulation. It over-writes lmer() with a new version, which gives p-values
library(glmmTMB) # for zero-inflated or hurdle glms
library(MuMIn) # for model selection and averaging
library(tidyverse) # data re-shaping, ggplot, stringr and more
library(ggridges) # for joy plots
library(ggpubr) # for the ggarrange function
library(ggbeeswarm) # violin plots with data points
library(ggResidpanel) # for model assumption plots
library(kableExtra) # nice tables that can scroll
library(pander) # more nice tables
library(groupdata2) # for assigning rows in data-frames to groups

# Read in data frame and add Dyad_ID column

all_data <- read.csv("mtDNA_larval_competition_data.csv") %>% 
  arrange(Individual) %>%
  group(n = 2, method = "greedy") %>% rename(Dyad_ID = .groups)

# helper function for saving large model objects and naming the file object.rds

save_it <- function(object){
  saveRDS(get(object), file = paste(object, ".rds", sep = ""))}

```


#### Data preparation for all responses

```{r data cleaning}

# Clean the dataset up for analysis

# Select the columns we're interested in and rename them

fitness_data <- dplyr::select(all_data, Individual, Block, Strain,  Dyad_ID, Sex, Focal.haplotype, Social.haplotype, Mortality, Development.time..hrs., Wing.size..mm., Female.offspring, Male.offspring, Total.female.assay, Total.red.all.vials, Total.bw.all.vials, Proportion.red.all.vials) %>% 
  
rename(Block = Block, Survived = Mortality, Focal_haplotype = Focal.haplotype, Social_haplotype = Social.haplotype, Dev_time = Development.time..hrs., Wing_length = Wing.size..mm., Maternal_female_offspring = Female.offspring, Maternal_male_offspring = Male.offspring, Maternal_total_offspring = Total.female.assay, Paternal_focal_offspring = Total.red.all.vials, Paternal_bw_offspring = Total.bw.all.vials, Proportion_focal = Proportion.red.all.vials)

# Define new levels for mortality to make renaming possible 

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "NO")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "YES")

# Rename the mortality responses
# L means died as larva, P means died as pupae, N means did not die (i.e. eclosed as an adult)

fitness_data$Survived[fitness_data$Survived == 'L'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'P'] <- 'NO'
fitness_data$Survived[fitness_data$Survived == 'N'] <- 'YES'

# Now that it makes sense change "YES" to 1 and "NO" to 0 so we can fit a binomial GLM.

levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "1")
levels(fitness_data$Survived) <- c(levels(fitness_data$Survived), "0")

fitness_data$Survived[fitness_data$Survived == "YES"] <- 1
fitness_data$Survived[fitness_data$Survived == "NO"] <- 0

# Make the factor numeric 

fitness_data$Survived <- as.numeric(as.character(fitness_data$Survived))


# Create specific datasets for each fitness trait

# Remove all rows that contain an NA value in the survival column. The NAs mean things like the GFP sorting did not work, or the vial was never set up due to a shortage of larvae. They are not meaningful data, and we remove them here.

survival <- fitness_data %>% filter(!is.na(Survived)) 
  
# Remove all rows that contain an NA value in the development time column. This instances represent flies where we failed to measure development time. 

larval_development <- fitness_data %>% filter(!is.na(Dev_time)) 

# Remove all rows that contain an NA value in the wing length column. Wing length was not measured in Blocks 1 and 2.

body_size <- fitness_data %>% filter(!is.na(Wing_length)) 

# Remove all rows that contain an NA value in the female reproductive output column (e.g. all the males), and where females did not survive to adulthood (coded as producing 0 offspring). 

female_reproductive_output <- fitness_data %>% filter(!is.na(Maternal_total_offspring), Survived == 1)


# Male adult fitness

# First remove females from the dataset.

Male_fitness <- fitness_data %>% filter(!is.na(Paternal_focal_offspring)) 

# Create an offspring counted column so that the data is correctly formatted for a binomial success-failure model.

Male_fitness$Offspring_counted <- Male_fitness$Paternal_focal_offspring + Male_fitness$Paternal_bw_offspring

# Now lets remove vials where the female produced 0 offspring (this includes trials where the male died in development), as we cannot determine paternity from these vials. The tidy up the dataframe by removing unneccessary columns

Male_fitness <- Male_fitness %>% filter(!(Offspring_counted == 0)) %>% 
  select(-Maternal_female_offspring, -Maternal_male_offspring, -Maternal_total_offspring) %>% 
  rename(Focal_male_offspring = Paternal_focal_offspring, bw_male_offspring = Paternal_bw_offspring)

```


## Modelling approach

We analysed the data using generalised linear mixed models in the `lmer` package for R.

**Fixed effects**

For the analysis of fitness traits expressed in both sexes (survival, development time and body size), we are interested in the effect of an individual’s focal mtDNA, the mtDNA of a social competitor and the effect of sex on fitness. To identify these potential effects each model contained the following fixed effects and the three-way interaction between these variables:

Focal haplotype: the mtDNA haplotype that an individual carries.

Social haplotype: the mtDNA haplotype carried by a social partner during larval development.

Sex: is the focal individual female or male? The social partner's sex was always opposite to that of the focal individual.

**Random effects**

Duplicate strain: Each haplotype has been introgressed alongside the _w^1118^_ nuclear background in two independent duplicates, creating 10 total strains. Within each block we ran multiple replicates that were made up of flies from the first set of strains (i.e. Barcelona 1, Brownsville 1 etc.), while the other half used only flies from strains denoted '2'. This random effect accounts for any residual differences in their nuclear genome, epigenome, microbiome or vial environment that may have arisen between duplicates.

Block: accounts for differences in the response variable between experimental blocks (e.g. to variance in temperature or composition of the fly food). In our experiment a block contained multiple replicates and a replicate was made up of 25 different cells each housing a pair of larvae.

Dyad ID: accounts for differences in the quality of the larval environment between pairs of larvae. For example, the moisture content of the food varied between pipette tips, despite our best efforts to keep this variable constant.  

**Model evaluation**

Each model was evaluated and ranked by AICc values using the `dredge` function, from the `Mumin` package. There was rarely a single model that was unequivocally the best fit to the data, so we conducted model averaging for the set of models where delta was < 6, as suggested by Symonds and Moussalli [-@RN455]. The present study is a planned experiment to measure the effect of mtDNA on fitness, so we derived model estimates from the conditional model averages.


## Larval fitness measures


### Egg to adult viability analysis
* * *

We fit a glm with binomial errors to model survival

The model:

_Survival ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_

```{r survival model}

# Fit the global model

survival_model <- lme4::glmer(Survived ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), data = survival, family = "binomial", control = glmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=100000)), na.action = na.fail)

```

#### Model evaluation

**Table S1**: Evaluation of the survivorship model. All possible models were evaluated from the global model that included a three-way interaction between Focal haplotype, Social haplotype and Sex, as well as the random factors duplicate Strain, Block and Dyad ID. As there was no clear top model, the final model was calculated via model averaging.
```{r survival dredge table}

# Compare all possible combinations of models (from the global model)

if(file.exists("survival_dredge.rds")){ # If already done, just load the results
  survival_dredge <- readRDS("survival_dredge.rds")
} else {survival_dredge <- dredge(survival_model) # If not already done, run all the models and save the results
lapply(c("survival_dredge"), save_it)
}


survival_table <- subset(survival_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(survival_table)[names(survival_table) == "(Intercept)"] <- "Intercept"
names(survival_table)[names(survival_table) == "Focal_haplotype"] <- "Focal haplotype"
names(survival_table)[names(survival_table) == "Sex"] <- "Sex"
names(survival_table)[names(survival_table) == "Social_haplotype"] <- "Social haplotype"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(survival_table)[names(survival_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(survival_table)[names(survival_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(survival_table)[names(survival_table) == "df"] <- "Degrees of freedom"
names(survival_table)[names(survival_table) == "logLik"] <- "Log likelihood"
names(survival_table)[names(survival_table) == "AICc"] <- "AICc"
names(survival_table)[names(survival_table) == "delta"] <- "Delta"
names(survival_table)[names(survival_table) == "weight"] <- "Weight"

pander(survival_table, split.cell = 40, split.table = Inf)
```

#### Model averaging


Conditional model coefficients, standard error and 95% confidence limits listed in **Table 1** are shown for the survivorship to adulthood averaged model. Bold rows indicate significant effects.

```{r survival model averaging}

# Model average

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict(), fitted() or eemeans()

top_survival_models <- get.models(survival_dredge, subset = delta < 6)

survival_avgm <- model.avg(top_survival_models)


# extract useful information

RVI_survival <- MuMIn::sw(survival_dredge)

# average the models with delta < 6

survival_CIs <- confint(model.avg(survival_dredge, subset = delta < 6)) %>% as.data.frame()

survival_estimate <- coefTable(model.avg(survival_dredge, subset = delta < 6)) %>% as.data.frame()

survival_model_avg <- data.frame(survival_estimate, survival_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(survival_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(survival_model_avg)[names(survival_model_avg) == "Estimate"] <- "Conditional average estimate"
names(survival_model_avg)[names(survival_model_avg) == "Std..Error"] <- "Standard Error"
names(survival_model_avg)[names(survival_model_avg) == "X2.5.."] <- "2.5% Interval"
names(survival_model_avg)[names(survival_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(survival_model_avg, split.cell = 40, split.table = Inf, round = 3)

# The full average provides a parameter average across all models considered, including ones where the parameter coefficient is set to 0. The conditional average reports coefficents for only the models where the parameter is included.

```

### Development time analysis
* * *

```{r dev time density plot}
(
density_development_plot <- ggplot(larval_development)+
  stat_density_ridges(aes(x=Dev_time, y = NA, fill = Sex), alpha = 0.7, scale = 12, position = position_nudge(y = -0.5), show.legend = T) +
  geom_vline(xintercept = 238, linetype = 2) +
  geom_vline(xintercept = 262, linetype = 2) +
  geom_vline(xintercept = 286, linetype = 2) +
  xlab("Egg-to-adult development time (hours)") +
  ylab("Kernel density estimate") +
  theme_bw() +
  scale_fill_manual(values = c("F" = "#ca562c", "M" = "#008080"), labels = c("Female", "Male")) +
  scale_x_continuous(limits = c(220, 310), breaks = c(220, 230, 240, 250, 260, 270, 280, 290, 300, 310)) +
  scale_y_discrete(expand = c(.0,0.0))+
  theme(panel.spacing = unit(0.1, "lines"),
        text = element_text(size=16),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.x = element_text(hjust = 0.5, size = 14))
)


```

**Figure S2**: The distribution of egg-to-adult development time, split by sex. Dashed lines indicate when lights were turned each morning and highlight the relationship between light and eclosion.

The response variable has a trimodal distribution, that can be potentially explained by the lab's artificial day-night cycle. When the lights turn on at 7am, eclosion is stimulated.

The model:

_Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_

```{r dev time model}

# Fit the linear model

linear_dev_model <- lmer(Dev_time ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), larval_development, na.action = na.fail, REML = FALSE)

```

Lets have a look at model diagnostics

```{r dev time diagnostic plot}
resid_panel(linear_dev_model)
```

The data is trimodal and the residuals vs fitted plot indicates that the mean and variance are weakly positively correlated. The Q-Q Plot shows that points fall off at the extremes, but generally conform to a linear pattern. Linear models are somewhat robust against slightly non-normal data so we proceed with the analysis.

#### Model evaluation

**Table S2**: Evaluation of the development time model. All possible models were evaluated from the global model that included a three-way interaction between Focal haplotype, Social haplotype and Sex as well as the random factors Strain and Block.  As there was no clear top model, the final model was calculated via model averaging.
```{r dev time dredge table}
# Use dredge to compare all possible models derived from the global model

Dev_time_linear_dredge <- dredge(linear_dev_model)

development_table <- subset(Dev_time_linear_dredge, delta < 6, recalc.weights = FALSE)  %>% as.data.frame()

names(development_table)[names(development_table) == "(Intercept)"] <- "Intercept"
names(development_table)[names(development_table) == "Focal_haplotype"] <- "Focal haplotype"
names(development_table)[names(development_table) == "Social_haplotype"] <- "Social haplotype"
names(development_table)[names(development_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(development_table)[names(development_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(development_table)[names(development_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(development_table)[names(development_table) == "df"] <- "Degrees of freedom"
names(development_table)[names(development_table) == "logLik"] <- "Log likelihood"
names(development_table)[names(development_table) == "AICc"] <- "AICc"
names(development_table)[names(development_table) == "delta"] <- "Delta"
names(development_table)[names(development_table) == "weight"] <- "Weight"

pander(development_table, split.cell = 40, split.table = Inf)

```

#### Model averaging

Conditional model coefficients, standard error and 95% confidence limits listed in **Table 2** are shown for the egg-to-adult development time averaged model. Bold rows indicate significant effects.

```{r dev time model averaging}

# first get the RVI for each predictor

RVI_dev <- MuMIn::sw(Dev_time_linear_dredge)

# Model averaging

Dev_time_avg <- (model.avg(Dev_time_linear_dredge, subset = delta < 6))

Dev_CIs <- confint(model.avg(Dev_time_linear_dredge, subset = delta < 6)) %>% as.data.frame()

Dev_estimate <- coefTable(model.avg(Dev_time_linear_dredge, subset = delta < 6)) %>% as.data.frame()

Dev_model_avg <- data.frame(Dev_estimate, Dev_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Dev_model_avg) <- c("Intercept", "Sex: Male", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Dev_model_avg)[names(Dev_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Dev_model_avg)[names(Dev_model_avg) == "Std..Error"] <- "Standard Error"
names(Dev_model_avg)[names(Dev_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Dev_model_avg)[names(Dev_model_avg) == "X97.5.."] <- "97.5% Interval"


pander(Dev_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)

```


## Adult fitness measures


### Body size analysis
* * *

We use wing length as a proxy for adult body size.


It is distributed normally, so we fit a linear mixed model.

The model:

_Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID)_


```{r size model}

body_size_model <- lmer(Wing_length ~ Focal_haplotype * Social_haplotype * Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

```



```{r size diagnostics,include=FALSE}

# Lets have a look at model diagnostics

resid_panel(body_size_model)
```

#### Model evaluation

**Table S3**: Evaluation of the wing length model. All possible models were evaluated from the global model that included a three-way interaction between focal haplotype, social haplotype and sex, as well as the random factors Duplicate strain, Block and Dyad ID. There was a clear top model; coefficients are displayed in Table S4.
```{r size dredge table}

# Compare all possible combinations of models (from the global model)

body_size_dredge <- dredge(body_size_model)

size_table <- subset(body_size_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()


names(size_table)[names(size_table) == "(Intercept)"] <- "Intercept"
names(size_table)[names(size_table) == "Focal_haplotype"] <- "Focal haplotype"
names(size_table)[names(size_table) == "Sex"] <- "Sex"
names(size_table)[names(size_table) == "Social_haplotype"] <- "Social haplotype"
names(size_table)[names(size_table) == "Focal_haplotype:Sex"] <- "Focal haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(size_table)[names(size_table) == "Sex:Social_haplotype"] <- "Social haplotype x Sex"
names(size_table)[names(size_table) == "Focal_haplotype:Sex:Social_haplotype"] <- "Focal haplotype x Social haplotype x Sex"
names(size_table)[names(size_table) == "df"] <- "Degrees of freedom"
names(size_table)[names(size_table) == "logLik"] <- "Log likelihood"
names(size_table)[names(size_table) == "AICc"] <- "AICc"
names(size_table)[names(size_table) == "delta"] <- "Delta"
names(size_table)[names(size_table) == "weight"] <- "Weight"

pander(size_table, split.cell = 40, split.table = Inf)

```

#### Best fitting model

There is a clear top model; model avergaing is not required.

Model coefficients, standard error and 95% confidence limits listed in **Table 3** are shown are shown for the wing length top model. Bold rows indicate significant effects.

```{r size best model}

# first get the RVI for each predictor

RVI_size <- MuMIn::sw(body_size_dredge)


# Fit the top model

body_size_model_final <- lmer(Wing_length ~ Sex + (1|Strain) + (1|Block) + (1|Dyad_ID), body_size, na.action = na.fail, REML = FALSE)

Size_CIs <- confint(body_size_model_final) %>%
  as.data.frame() %>% 
  slice(5:6)

Size_estimate <- coefTable(body_size_model_final) %>% as.data.frame()

Size_model_avg <- data.frame(Size_estimate, Size_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Size_model_avg) <- c("Intercept", "Sex: Male")

names(Size_model_avg)[names(Size_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Size_model_avg)[names(Size_model_avg) == "Std..Error"] <- "Standard Error"
names(Size_model_avg)[names(Size_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Size_model_avg)[names(Size_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Size_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = (2), round = 3)

```


### Female reproductive output
* * *

To effectively accommodate zero-inflation, we modelled female offspring production using the `glmmTMB` package [@RN602]. This package allows us to fit hurdle models and zero-inflated models.

Hurdle models treat zero-count and nonzero outcomes as two completely separate categories, while zero-inflated models treat zero-count outcomes as a mixture of structural and sampling zeros.

We analysed the number of offspring produced by females using a hurdle model with negative binomial errors. This approach allowed us to answer two questions: (1) did mtDNA and/or competition affect the incidence of failing to produce any offspring? and (2) for females that produced at least one offspring, was the number of offspring produced affected by mtDNA/competition?

The model:

_Maternal_total_offspring ~ Focal_haplotype * Social_haplotype + (1|Strain) + (1|Block)_

```{r female model}
female_hurdle_model <- glmmTMB(Maternal_total_offspring ~ Social_haplotype * Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~., na.action = na.fail, REML = FALSE)
```

#### Model evaluation

**Table S4**: Evaluation of the female reproductive output model. All possible models were evaluated from the global model that included an interaction between Focal haplotype and Social haplotype and the random factors Strain and Block. As there was no clear top model, the final model was calculated via model averaging. The zero-inflated results correspond to question (1), while the conditional results correspond to question (2) above.
```{r female dredge table}
# Compare all possible combinations of models (from the global model)

if(file.exists("female_dredge.rds")){ # If already done, just load the results
  female_dredge <- readRDS("female_dredge.rds")
} else {female_dredge <- dredge(female_hurdle_model)                  # If not already done, run all the models and save the results
lapply(c("female_dredge"), save_it)
}


female_table <- subset(female_dredge, delta < 6, recalc.weights = FALSE) %>% as.data.frame()

names(female_table)[names(female_table) == "cond((Int))"] <- "Conditional intercept"
names(female_table)[names(female_table) == "zi((Int))"] <- "Zero-inflated intercept"
names(female_table)[names(female_table) == "disp((Int))"] <- "Dispersion factor intercept"
names(female_table)[names(female_table) == "cond(Focal_haplotype)"] <- "Conditional (Focal haplotype)"
names(female_table)[names(female_table) == "cond(Social_haplotype)"] <- "Conditional (Social haplotype)"
names(female_table)[names(female_table) == "cond(Focal_haplotype:Social_haplotype)"] <- "Conditional (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype)"] <- "Zero-inflated (Focal haplotype)"
names(female_table)[names(female_table) == "zi(Social_haplotype)"] <- "Zero-inflated (Social haplotype)"
names(female_table)[names(female_table) == "zi(Focal_haplotype:Social_haplotype)"] <- "Zero-inflated (Focal haplotype x Social haplotype)"
names(female_table)[names(female_table) == "df"] <- "Degrees of freedom"
names(female_table)[names(female_table) == "logLik"] <- "Log likelihood"
names(female_table)[names(female_table) == "AICc"] <- "AICc"
names(female_table)[names(female_table) == "delta"] <- "Delta"
names(female_table)[names(female_table) == "weight"] <- "Weight"

pander(female_table, split.cell = 40, split.table = Inf)

```

#### Model averaging

Zi (zero-hurdle requirement) and conditional (after hurdle)  model coefficients, standard error and 95% confidence limits listed in **Table 4** are shown for the female offspring production averaged model. Bold rows indicate significant effects. 

```{r female model averaging}

# We need to create the top_survival_models object and average from that so that we can get mean estimates successfully using predict()

top_female_models <- get.models(female_dredge, subset = delta < 6)

female_avgm <- model.avg(top_female_models)

# extract useful info

RVI_female <- MuMIn::sw(female_dredge)

Female_CIs <- confint(model.avg(female_dredge, subset = delta < 6)) %>% as.data.frame()

Female_estimate <- coefTable(model.avg(female_dredge, subset = delta < 6)) %>% as.data.frame()

Female_model_avg <- data.frame(Female_estimate, Female_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Female_model_avg) <- c("Conditional intercept", "Conditional focal haplotype: Brownsville", "Conditional focal haplotype: Dahomey", "Conditional focal haplotype: Israel", "Conditional focal haplotype: Sweden", "Zi intercept", "Zi social haplotype: Brownsville", "Zi social haplotype: Dahomey", "Zi social haplotype: Israel", "Zi social haplotype: Sweden", "Zi focal haplotype: Brownsville", "Zi focal haplotype: Dahomey", "Zi focal haplotype: Israel", "Zi focal haplotype: Sweden")


names(Female_model_avg)[names(Female_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Female_model_avg)[names(Female_model_avg) == "Std..Error"] <- "Standard Error"
names(Female_model_avg)[names(Female_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Female_model_avg)[names(Female_model_avg) == "X97.5.."] <- "97.5% Interval"

Female_model_avg %>%
  pander(split.cell = 40, split.table = Inf, emphasize.strong.rows = c(2, 5, 9), round = 3)

```


```{r female figure, fig.width=11, fig.height=8.5}

# Plotting with model predictions

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype in the Zi formual. These were found by inspection of the top model list above.

focal_female_zi_models <- get.models(female_dredge, subset = "26")

# Note that only model "26' contains focal haplotype in the Zi formula. No averaging takes place and estimates are derived straight from this model. The conditional averaged estimates from the female_avgm object are identical to the estimates in model "26".

# fit model "26"

focal_zi_female_avg <- glmmTMB(Maternal_total_offspring ~ Focal_haplotype + (1|Strain) + (1|Block), data = female_reproductive_output, family = list(family="truncated_nbinom1",link="log"), ziformula = ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block), na.action = na.fail, REML = FALSE)


# Now average models that contain the social haplotype predictor in the Zi formula.

social_female_zi_models <- get.models(female_dredge, subset = c("18", "17", "26"))

social_zi_female_avg <- model.avg(social_female_zi_models)

# The conditional averaged estimates from the female_avgm object are identical to the zi social haplotype estimates from the "full model "social_zi_female_avg" object.

# Now average models that contain the focal haplotype predictor in the conditional formula.

focal_female_con_models <- get.models(female_dredge, subset = c("18", "2", "26"))

focal_con_female_avg <- model.avg(focal_female_con_models)

# Estimates match female_avg

# Make a new dataframe, for which we will derive predictions. It's the same as the old data, except that we set Focal haplotype, block and duplicate to the same value for all observations. The re.form = NA argument sets random effects to 0, meaning population means are calculated.
 
new_data <- female_reproductive_output %>%
  ungroup() %>%
  select(Focal_haplotype, Strain, Block) %>%
  mutate(Social_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# First lets get predictions for the average number of offspring produced by females that produced at least one progeny, split by focal haplotype.

pred <- predict(focal_con_female_avg, se.fit = TRUE, type = "conditional", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred1 <- pred %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred2 <- pred %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred <- cbind(new_data, pred1, pred2) %>%
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE) %>%
  rename(Maternal_total_offspring = mean_estimate)

# Load the data for each individual female that produced offspring so that this can be plotted

female_cond_plot_data <- female_reproductive_output %>% 
  filter(Maternal_total_offspring != 0) %>%
  ungroup() %>% 
  select(Individual, Focal_haplotype, Maternal_total_offspring)

# Now lets plot these predictions

female_focal_cond_plot <- female_cond_plot_data %>%
  ggplot(aes(x = Focal_haplotype, y = Maternal_total_offspring, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = female_cond_plot_data, width = 0.3, size = 2, alpha =  0.5, pch = 21, colour = 'grey26') +
scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
geom_point(data = pred, aes(x = Focal_haplotype, y = Maternal_total_offspring), size = 3, colour='black') +
  geom_errorbar(data = pred, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Number of offspring produced by females") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

# Now lets get the Zi predictions for focal haplotype

pred_ZI <- predict(focal_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_1 <- pred_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_2 <- pred_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI <- cbind(new_data, pred_ZI_1, pred_ZI_2) %>%
  transmute(Focal_haplotype, Strain, Block, Social_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
#rename(Maternal_total_offspring = mean_estimate)


# Plot
  
female_focal_zi_plot <- pred_focal_ZI %>%
  ggplot(aes(x = Focal_haplotype, y = mean_estimate, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_errorbar(aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Focal_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26', fill = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Female mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())
  

# Now create the newdata for social haplotype predictions

new_data_social <- female_reproductive_output %>%
  ungroup() %>%
  select(Social_haplotype, Strain, Block) %>%
  mutate(Focal_haplotype = "Barcelona", Strain = "Barcelona 1", Block = "1") %>% 
  distinct()

# Get zi social haplotype predictions

pred_social_ZI <- predict(social_zi_female_avg, se.fit = TRUE, type = "zprob", re.form = NA, new_data_social) %>%
  unlist() %>% 
  as.data.frame()

pred_ZI_social_1 <- pred_social_ZI %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_ZI_social_2 <- pred_social_ZI %>% 
  slice(6:10) %>% 
  rename(SE = ".")

pred_focal_ZI_social <- cbind(new_data_social, pred_ZI_social_1, pred_ZI_social_2) %>% 
  transmute(Social_haplotype, Strain, Block, Focal_haplotype, mean_estimate  = 1 - mean_estimate, SE) %>% 
  mutate(Upper = mean_estimate + SE,
         Lower = mean_estimate - SE)
  
  # Plot 
  
female_social_zi_plot <- pred_focal_ZI_social %>%
  ggplot(aes(x = Social_haplotype, y = mean_estimate, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_errorbar(aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  geom_point(aes(x = Social_haplotype, y = mean_estimate), size = 4, pch =21, colour='grey26', fill = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  labs(x = "Male mtDNA haplotype", y = "Proportion of females producing offspring") +
  ylim(0.4, 1) +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(female_focal_zi_plot, female_social_zi_plot, female_focal_cond_plot, labels = c("a", "b", "c"))
  
```

**Figure 1:** mtDNA directly and indirectly affects female fitness. Panels **a** and **b** show model predictions of the mean proportion of females that produced offspring (the zero-inflated or hurdle component of the model) across **a** female focal haplotypes and **b** social male haplotypes. Error bars depict standard errors. Panel **c** shows the direct effect of mtDNA on the number of offspring produced by a female. Black points show model predictions of the mean with standard error for each haplotype, while coloured points represent offspring produced by individual females.


### Male adult fitness
* * *

Our measure of male fitness involves both pre- and post-copulatory competitive ability; that is we assess in one measure the combination of 1) the ability of a male to inseminate a female in the presence of another male and 2) the competitive ability of his sperm within females that have been inseminated by another male. Interestingly, the data contains many 0 or 1 values - corresponding to a monopoly of female fertilisation by one of the males. *SOMETHING ABOUT THE BETA BINOMIAL DISTRIBUTION *

We analyse male fitness as the proportion of offspring produced by mitochondrial strain males competing against a standard _bw_ male competitor. 

The Brownsville haplotype renders males sterile alongside the _w^1118^_ nuclear background and sub-fertile alongside all other tested backgrounds. In our experiment, we find that Brownsville males are able to produce offspring but to a very limited capacity. Due to this, our model is unable to produce reliable estimates when the interaction between focal and social haplotype is included. We do not include the interaction in the full model.

_(Focal_male_offspring, bw_offspring) ~ Focal_haplotype + Social_haplotype + (1|Strain) + (1|Block) + (1|Individual)_ 

```{r male model}

response <- cbind(Male_fitness$Focal_male_offspring, Male_fitness$bw_male_offspring)

male_binary_model <- glmmTMB(response ~ Focal_haplotype + Social_haplotype + (1|Block) + (1|Strain) + (1|Individual), data = Male_fitness, family = "betabinomial", na.action = na.fail)

```

#### Model evaluation


**Table S5**: Evaluation of the male adult fitness model. All possible models were evaluated from the global model that included an interaction between focal haplotype and social haplotype and the random factors Strain, Block and Individual. As there was no clear top model, the final model was calculated via model averaging.
```{r male dredge table}

male_dredge <- dredge(male_binary_model)

Male_table <- subset(male_dredge, delta < 6) %>% as.data.frame()


names(Male_table)[names(Male_table) == "(Intercept)"] <- "Intercept"
names(Male_table)[names(Male_table) == "Focal_haplotype"] <- "Focal haplotype"
names(Male_table)[names(Male_table) == "Social_haplotype"] <- "Social haplotype"
names(Male_table)[names(Male_table) == "Focal_haplotype:Social_haplotype"] <- "Focal haplotype x Social haplotype"
names(Male_table)[names(Male_table) == "df"] <- "Degrees of freedom"
names(Male_table)[names(Male_table) == "logLik"] <- "Log likelihood"
names(Male_table)[names(Male_table) == "AICc"] <- "AICc"
names(Male_table)[names(Male_table) == "delta"] <- "Delta"
names(Male_table)[names(Male_table) == "weight"] <- "Weight"

pander(Male_table, split.cell = 40, split.table = Inf)

```


#### Model averaging

Model coefficients, standard error and 95% confidence limits listed in **Table 5** are shown for the male adult fitness averaged model. Bold rows indicate significant effects. 

```{r male model averaging}

# Model average

top_male_models <- get.models(male_dredge, subset = delta < 6)

male_avgm <- model.avg(top_male_models)

# extract useful information

RVI_male <- MuMIn::sw(male_dredge)


# summary(model.avg(male_binary_dredge, subset = delta < 6))

Male_CIs <- confint(model.avg(male_dredge, subset = delta < 6)) %>% as.data.frame()

Male_estimate <- coefTable(model.avg(male_dredge, subset = delta < 6)) %>% as.data.frame()

Male_model_avg <- data.frame(Male_estimate, Male_CIs) %>% select(Estimate, Std..Error,  X2.5.., X97.5..)

row.names(Male_model_avg) <- c("Intercept", "Focal haplotype: Brownsville", "Focal haplotype: Dahomey", "Focal haplotype: Israel", "Focal haplotype: Sweden", "Social haplotype: Brownsville", "Social haplotype: Dahomey", "Social haplotype: Israel", "Social haplotype: Sweden")

names(Male_model_avg)[names(Male_model_avg) == "Estimate"] <- "Conditional average estimate"
names(Male_model_avg)[names(Male_model_avg) == "Std..Error"] <- "Standard Error"
names(Male_model_avg)[names(Male_model_avg) == "X2.5.."] <- "2.5% Interval"
names(Male_model_avg)[names(Male_model_avg) == "X97.5.."] <- "97.5% Interval"

pander(Male_model_avg, split.cell = 40, split.table = Inf, emphasize.strong.rows = 2, round = 3)

```


```{r male figure, fig.width= 8.5, fig.height= 6}

# predict.averaging does not return predictions for the conditional estimates (i.e. model coefficients averaged over models that contain the relevant predictor, rather than over the full specified subset). To predict mean estimates for each categorical variable, I can get these model averaged estimates by manually specifying the models I want to be avergaged. These are used only for plotting.

# First average models that contain the predictor focal haplotype. These were found by inspection of the top model list above.

focal_male_models <- get.models(male_dredge, subset = c("2", "4"))

focal_male_avg <- model.avg(focal_male_models)

# Note that the conditional averaged estimates from the male_avgm object are identical to the full averaged estimates for the focal_male_avg object for focal haplotype.

# Now average models that contain the social haplotype predictor.

social_male_models <- get.models(male_dredge, subset = c("4"))

# Note that there is only one model (the original full model) that contains social haplotype in the < 6 delta subset, so estimates are calculated directly from this model - no averaging occurs. The conditional averaged estimates from the male_avgm object are identical to the estimates from the full model.



# Focal new data

new_data_male <- Male_fitness %>%
  ungroup() %>%
  select(Focal_haplotype, Block, Strain, Individual) %>%
  mutate(Social_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Individual = "4") %>% 
  distinct() 


pred_male_focal <- predict(focal_male_avg, newdata = new_data_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_focal_1 <- pred_male_focal %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_focal_2 <- pred_male_focal %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_focal_male <- cbind(new_data_male, pred_male_focal_1, pred_male_focal_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)

# Plot

Male_focal_plot <- Male_fitness %>%
  ggplot(aes(x = Focal_haplotype, y = Proportion_focal, fill = Focal_haplotype, colour = Focal_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
  scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_focal_male, aes(x = Focal_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_focal_male, aes(x = Focal_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Male mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())


# Social new data

new_data_social_male <- Male_fitness %>%
  ungroup() %>%
  select(Social_haplotype, Block, Strain, Individual) %>%
  mutate(Focal_haplotype = "Barcelona", Block = "1", Strain = "Barcelona 1", Individual = "4") %>% 
  distinct()

# predict.averaging works over the full average rather than the conditional average that we present. I use a workaround where I create another model average object but only using the models in the < 6 delta subset that include social haplotype. Here only two models make the cut - the full model is the only one containing social haplotype as a predictor so no averaging is neccessary. Plug the full model into the predict function.

pred_male_social <- predict(male_binary_model, newdata = new_data_social_male, type = "response", se.fit = TRUE, re.form = NA) %>%
  unlist() %>% 
  as.data.frame()

pred_male_social_1 <- pred_male_social %>% 
  slice(1:5) %>% 
  rename(mean_estimate = ".")

pred_male_social_2 <- pred_male_social %>% 
  slice(6:10) %>% 
  rename(SE = ".")
  
pred_male_social <- cbind(new_data_social_male, pred_male_social_1, pred_male_social_2) %>% 
  rename(Proportion_focal = mean_estimate) %>% 
  mutate(Upper = Proportion_focal + SE,
         Lower = Proportion_focal - SE)
  

# Plot

Male_social_plot <- Male_fitness %>%
  ggplot(aes(x = Social_haplotype, y = Proportion_focal, fill = Social_haplotype, colour = Social_haplotype)) +
  geom_quasirandom(data = Male_fitness, width = 0.3, alpha =  0.3, pch = 21, colour = 'grey21', aes(size = Offspring_counted)) +
  scale_size_continuous(range = c(0.5, 6), labels = NULL, breaks = c(20, 40, 60, 80, 100, 120)) +
 scale_fill_manual(values = c("Barcelona" = "#fcde9c", "Brownsville" = "#f58670", "Dahomey" = "#e34f6f", "Israel" = "#d72d7c" , "Sweden" = "#7c1d6f")) +
  geom_point(data = pred_male_social, aes(x = Social_haplotype, y = Proportion_focal), size = 3, colour='black') +
  geom_errorbar(data = pred_male_social, aes(x = Social_haplotype, ymax = Upper, ymin = Lower, width = 0), colour = "black") +
  labs(x = "Female mtDNA haplotype", y = "Proportion of offspring sired by focal male") +
  theme_minimal() +
  theme(legend.position = "none") +
  theme(panel.grid.major.x = element_blank())

ggarrange(Male_focal_plot, Male_social_plot, labels = c("a", "b"))

```

**Figure 2**: The proportion of offspring produced by _mt_-strain males competing with standard _bw_ males. **a** shows the direct effect of mtDNA on male fitness. **b** shows the indirect genetic effect of female mtDNA on male fitness. Coloured points represent individual males, with larger points indicating a higher number of offspring produced in the vial (sired by either male). Black points show model predictions of the mean proportion of offspring sired by the _mt_-strain male, with associated standard errors.


# Raw data and reproducibility

### Table of raw data

For the purposes of completeness, transparency and data archiving, we include the raw data in this report.


**Table S6**: the raw data-set used in the present study, with NA values resulting from data collection mistakes removed (i.e. two females placed in competitive environment, no value recorded for whether the fly survived, flies that escaped during the experiment etc.).
```{r raw data}
kable(fitness_data %>% filter(!is.na(Survived)), "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "800px")
```


Columns represent:

**Individual:** the focal fly being tested.

**Block:** the distinct period of time that the particular individual was tested.

**Strain:** Which of the 10 combinations of haplotype and duplicate strain was the individual from?

**Replicate:** A set of all possible combinations of the 5 haplotypes. Each replicate contained 25 cells.

**Sex:** was the focal individual female or male?

**Focal_haplotype:** what mtDNA haplotype did the focal individual carry?

**Social_haplotype:** what mtDNA haplotype did the social competitor of the focal individual carry?

**Survived:** did the focal individual survive to adulthood (1) or die during larval development (0)?

**Social_survival:** did the social competitor die as a larva (L), as a pupa (P) or survive to adulthood (N)?

**Dev_time:** how many hours did it take for the focal individual to progress from an egg to an adult. NA values indicate where individuals did not survive or development time could not be measured.

**Day:** how many days did it take the focal individual to develop?

**Hours:** lights were turned on at 7am every morning. How many hours did it take for individuals to eclose after this time?

**Wing_length:** what was the length in mm of the focal individuals right wing?

**Maternal_female_offspring:** how many adult female offspring did a focal female produce in a two day period?

**Maternal_male_offspring:** how many adult male offspring did a focal female produce in a two day period?

**Maternal_total_offspring:** how many adult offspring did a focal female produce in a two day period?

**Paternal_focal_offspring:** how many red-eye phenotype offspring were there across the two vials in the male adult fitness assay?

**Paternal_bw_offspring:** how many brown-eye phenotype offspring were there across the two vials in the male adult fitness assay?


### R session information

This section provides information on the operating system and R packages attached during the production of this document, to allow easier replication of the analysis.

```{r session info}
sessionInfo() %>% pander
```


# References
